SOEPcompanion (v38)

Kara, Selin; Zimmermann, Stefan Research Report SOEPcompanion (v38) SOEP Survey Papers, No. 1261 Provided in Cooperation with: German Institute for Economic Research (DIW Berlin) Suggested Citation: Kara, Selin; Zimmermann, Stefan (2023) : SOEPcompanion (v38), SOEP Survey Papers, No. 1261, Deutsches Institut für Wirtschaftsforschung (DIW), Berlin This Version is available at: https://hdl.handle.net/10419/273538 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-sa/4.0/ 1261 2023 Series F – General Issues and Teaching Materials SOEPcompanion (v38) Selin Kara, Stefan Zimmermann, and SOEP Group Running since 1984, the German Socio-Economic Panel study (SOEP) is a wide-ranging representative longitudinal study of private households, located at the German Institute for Economic Research, DIW Berlin. The aim of the SOEP Survey Papers Series is to thoroughly document the survey’s data collection and data processing. The SOEP Survey Papers is comprised of the following series: Series A – Survey Instruments (Erhebungsinstrumente) Series B – Survey Reports (Methodenberichte) Series C – Data Documentation (Datendokumentationen) Series D – Variable Descriptions and Coding Series E – SOEPmonitors Series F – SOEP Newsletters Series G – General Issues and Teaching Materials The SOEP Survey Papers are available at http://www.diw.de/soepsurveypapers Editors: Dr. Carina Cornesse, DIW Berlin and University of Bremen Dr. Jan Goebel, DIW Berlin Prof. Dr. Cornelia Kristen, University of Bamberg and DIW Berlin Prof. Dr. Philipp Lersch, DIW Berlin and Humboldt-Universität zu Berlin Prof. Dr. Carsten Schröder, DIW Berlin and Freie Universität Berlin Prof. Dr. Jürgen Schupp, DIW Berlin and Freie Universität Berlin Prof. Dr. Sabine Zinn, DIW Berlin and Humboldt-Universität zu Berlin Please cite this paper as follows: Selin Kara, Stefan Zimmermann, and SOEP Group, 2023. SOEPcompanion (v38). SOEP Survey Papers 1261: Series F – General Issues and Teaching Materials. Berlin: DIW Berlin/SOEP This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. © 2023 by SOEP ISSN: 2193-5580 (online) DIW Berlin German Socio-Economic Panel (SOEP) Mohrenstr. 58 10117 Berlin Germany [email protected] The German Socio Economic Panel study at DIW Berlin SOEPcompanion (v38) Selin Kara, Stefan Zimmermann, and SOEP Group 2023 SOEPcompanion Release 2023 Selin Kara, Stefan Zimmermann, SOEP Group Jul 24, 2023 SOEP Survey Paper 1261SOEP Survey Paper 1261 CONTENTS 1 Preface 1 2 Topics of SOEP-Core 2 2.1 Demography and Population ....................................... 3 2.2 Work and Employment .......................................... 4 2.3 Income, Taxes, and Social Security ................................... 15 2.4 Family and Social Networks ....................................... 29 2.5 Health and Care ............................................. 37 2.6 Home, Amenities, and Contributions of Private HH ........................... 43 2.7 Education and Qualification ....................................... 50 2.8 Attitudes, Values, and Personality .................................... 61 2.9 Time Use and Environmental Behavior ................................. 66 2.10 Integration, Migration, Transnationalization ............................... 71 2.11 Survey Methodology ........................................... 74 3 Survey Design 75 3.1 SOEP Questionnaires .......................................... 75 3.1.1 Overview of the Questionnaires ................................ 77 3.1.2 Household Questionnaire .................................... 77 3.1.3 Individual Questionnaire .................................... 79 3.1.4 Biography Questionnaire .................................... 82 3.1.5 Mother and Child Instruments ................................. 83 3.1.6 Youth Instruments ........................................ 85 3.1.7 Additional Instruments ..................................... 88 3.2 Scales Manual .............................................. 90 3.2.1 Affective Well-Being ...................................... 90 3.2.2 Anomie ............................................. 92 3.2.3 Basic Social Justice Orientations Scale ............................. 94 3.2.4 Cognitive Competencies .................................... 96 3.2.5 Conspiracy Mentality ...................................... 99 3.2.6 Effort-Reward Imbalance Model ................................ 100 3.2.7 Impulsiveness & Patience .................................... 103 3.2.8 Life Goals ............................................ 104 3.2.9 Life Satisfaction ......................................... 108 3.2.10 Locus of Control ........................................ 118 3.2.11 Loneliness ............................................ 120 3.2.12 Narcissistic Admiration and Rivalry Questionnaire Short Scale (NARQ-S) .......... 121 3.2.13 Optimism/Pessimism – Attitudes toward the Future ...................... 123 3.2.14 Parenting Goals ......................................... 124 3.2.15 Parenting Role ......................................... 125 i SOEP Survey Paper 1261SOEP Survey Paper 1261 3.2.16 Parenting Style ......................................... 127 3.2.17 Patient Health Questionnaire – 4 (PHQ-4) ........................... 129 3.2.18 Personality – Big Five ...................................... 131 3.2.19 Reciprocity ........................................... 139 3.2.20 Risk Aversion .......................................... 142 3.2.21 Self Esteem ........................................... 145 3.2.22 Sources of Social Inequality .................................. 146 3.2.23 Strengths and Difficulties Questionnaire (SDQ) ........................ 147 3.2.24 Supportive Parenting ...................................... 152 3.2.25 Temperament .......................................... 154 3.2.26 Tendency to Forgive ...................................... 155 3.2.27 Trust, Trustworthiness, Fairness ................................ 156 3.2.28 Vineland Adaptive Behavior Scales .............................. 158 3.3 Survey Concepts and Modes ....................................... 161 3.4 Panel Care ................................................ 162 4 Target Population and Samples 164 4.1 The SOEP Samples in Detail ....................................... 165 4.1.1 Sample-Specific Questionnaires ................................ 168 4.2 Eligibility and Follow-up ......................................... 175 4.3 Development of Sample Sizes ...................................... 176 5 Data Structure of SOEP-Core 179 5.1 Data Editions of SOEP-Core ....................................... 179 5.1.1 Teaching, International, and EU Edition ............................ 180 5.1.2 Add-ons: Area Types and Planning Regions .......................... 180 5.1.3 Remote Edition ......................................... 180 5.1.4 Onsite Edition .......................................... 181 5.2 Principles of Data Analysis ....................................... 181 5.2.1 Cross-Sectional Data Structure (CS) .............................. 182 5.2.2 Data Structure in “Wide” Format (wide) ............................ 182 5.2.3 Data Structure in “Long” Format (long) ............................ 182 5.2.4 Data Structure in Spell Format (spell) ............................. 183 5.3 Data Distribution File .......................................... 183 5.3.1 Core Datasets .......................................... 186 5.3.2 Raw Datasets .......................................... 186 5.3.3 eu-silc-like-panel ........................................ 188 5.4 Datasets SOEP-Core ........................................... 189 5.4.1 Tracking Data .......................................... 190 5.4.2 Original Data .......................................... 192 5.4.3 Survey Data ........................................... 194 5.4.4 Generated Data ......................................... 195 5.4.5 Spell Data ............................................ 199 5.5 Data Processing ............................................. 201 5.6 Dataset Identifiers ............................................ 201 5.6.1 Partner Identifier ........................................ 202 5.6.2 Family Identifier ........................................ 203 5.6.3 Interviewer Identifier ...................................... 205 5.7 Versioning and Harmonization ...................................... 205 5.8 Missing Conventions ........................................... 206 6 Working with SOEP Data 208 6.1 Working with Tracking Data (PPATHL) ................................. 208 6.2 Generating a Cross-Sectional Dataset .................................. 220 ii SOEP Survey Paper 1261SOEP Survey Paper 1261 6.3 Syntax Generator on paneldata.org ................................... 228 6.4 Generating a Longitudinal Dataset .................................... 235 6.5 Working with harmonized Variables ................................... 249 6.6 Longitudinal Data Analysis ....................................... 264 6.6.1 Clean and inspect the data ................................... 265 6.6.2 Univariate inspection & analysis ................................ 268 6.6.3 Simple cross sectional analyses ................................. 273 6.7 Working with Migration Data (BIOIMMIG) .............................. 276 6.8 Fixed Effects Estimation ......................................... 285 6.9 Working with SOEP Regional Data ................................... 299 6.10 Working with spatial data in R ...................................... 306 6.10.1 Prerequisites .......................................... 306 6.10.2 Reading data .......................................... 308 6.10.3 Transformations ......................................... 312 6.10.4 Plotting Spatial Data ...................................... 313 6.10.5 Frequently Used Operations .................................. 314 6.10.6 Complete Example ....................................... 321 6.10.7 Appendix ............................................ 325 6.11 How to Use SOEP IGEL ......................................... 326 6.11.1 IGEL Workstation ........................................ 326 6.11.2 Logging in ........................................... 326 6.11.3 Working with SOEP DATA ................................... 331 6.11.4 Importing Scripts or External Data ............................... 332 6.11.5 Instructions for exporting from Hauser to user ......................... 333 6.11.6 Data transfer from Moran to Hauser .............................. 335 6.12 Working with SOEP data in csv format ................................. 336 6.13 How to Merge SOEP Data in Stata .................................... 339 6.13.1 1:1 merge - one-to-one on key variables ............................ 340 6.13.2 1:m merge - one-to-many on key variables ........................... 343 6.13.3 m:1 merge – many-to-one on key variables ........................... 346 6.13.4 joinby .............................................. 348 7 Working with SOEP Documentation 350 7.1 Variable Search with Questionnaires ................................... 350 7.2 Variable Search with paneldata.org ................................... 352 7.3 Topic Search with paneldata.org ..................................... 360 7.4 Documentation on Generated Data ................................... 367 7.5 Working with SOEPhelp ......................................... 373 7.5.1 Working with SOEPhelp in R .................................. 373 7.5.2 Working with SOEPhelp in STATA .............................. 375 7.6 Working with Metadata-based Questionnaires .............................. 382 8 Contact Information 384 iii SOEP Survey Paper 1261SOEP Survey Paper 1261 CHAPTER ONE PREFACE SOEP-Core is the centerpiece of the Socio-Economic Panel, a wide-ranging representative longitudinal study of private households in Germany, based at the German Institute for Economic Research, DIW Berlin. SOEP-Core was started in 1984, and in 1990—shortly after German reunification—it was enlarged to include a representative sample from East Germany. This feature makes the SOEP unique among household panel surveys worldwide. Every year since 1984, individuals in households have been surveyed by the SOEP’s fieldwork organization, infas Institut für angewandte Sozialwissenschaften GmbH. The data provide information on every member of every household taking part in the survey. Respondents include Germans living in both the former East and West Germany, foreign citizens residing in Germany, recent immigrants, and a new sample of refugees added in 2016. Some of the many topics include household composition, education, occupational biographies, employment, earnings, health, and satisfaction indicators. The SOEPcompanion describes the current version of the SOEP-Core data (v38) and introduces users to the different SOEP-Core data structures. It also provides applications in Stata as well as instructions on how to use our various documentation services. We plan to revise the information in the SOEPcompanion annually to continue providing users a comprehensive, up-to-date introductory understanding of the SOEP. We know that starting to use any new dataset is difficult, and this is especially true of panel data given their complexity. We hope that this introduction will help. We always welcome any feedback or tips on how to improve our documentation. •Recommendation of our most recent version of a general short description of SOEP study: The German Socio- Economic Panel Study (SOEP) •To the information system for efficient working with complex datasets: paneldata.org 1 SOEP Survey Paper 1261SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Table 2 – continued from previous page Questionnaire Module Years Variables Preview Employment / education calendar, Vocational Training [2020] pab0003_v2 Employment status [1984-2020], [1984], [1985-1990], [1990], [1991-1995], [1996- 1998], [1999], [2000- 2001], [2002-2015], [2016-2021] plb0022_h,plb0022_v1, plb0022_v2, plb0022_v3, plb0022_v4, plb0022_v5, plb0022_v6, plb0022_v7, plb0022_v8, plb0022_v9 Employment, October 2014 [2015] plb0574 Entitlement to paid breaks [2015-2018] plb0601,plb0602, plb0603 Evening and weekend work, Evening [1990], [2012], [2013], (irregular) [1995-2019] plb0205_v1, plb0205_v2, plb0205_v3, plb0205_v4 [2023] Evening and weekend work, Night [1990], [2012], [2013], (irregular) [1995-2019] plb0206_v1, plb0206_v2, plb0206_v3, plb0206_v4 [2023] Evening and weekend work, Saturday (irregular) [2005-2019] plb0218 [2023] Evening and weekend work, Sunday (irregular) [2005-2019] plb0219 [2023] Financial compensation for overtime [1984-1986,1988- 2014,2018,2020], [1984-1986,1988- 1995], [1996], [1997- 2014,2018,2020] plb0195_h,plb0195_v1, plb0195_v2, plb0195_v3 [2022] Gross / net income, October 2014 [2015] plb0584,plb0585 Income from Internet activities [2020] pintver,pintverx, pintvstd1,pintvstd2, pintvstdno,pintvv1, pintvv2,pintvvno,pintet,pintetx,pintetstd1, pintetstd2,pintetstdno, pintetv1,pintetv2,pintetvno,pintba,pintbax, pintbastd1,pintbastd2, pintbastdno continues on next page 8 Chapter 2. Topics of SOEP-Core SOEP Survey Paper 1261SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Table 2 – continued from previous page Questionnaire Module Years Variables Preview Industry sector, occupational classification [2013-2020], [1990- 1993], [2020], [1999- 2021] p_isco08,p_nace, plb0072_v1, plb0072_v2, plb0072_v3, plb0073_h,plb0073_v1, plb0073_v2, plb0073_v3, plb0073_v4, plb0073_v5 [2022, 2023] Job search [1985-2020] plb0358_h Job search, Active Search [1989-2020] plb0362 [2022, 2023] Job search, Applied on Speculation [1989-1998] plb0358_v8 Job search, Friends / Acquaintances [1985-1998], [1985- 1988] plb0358_v3, plb0358_v5 Job search, Job Centre [1985-1998] plb0358_v1 Job search, Learn about current Position [1999-2002], [2003- 2013], [2014], [2015- 2021] plb0358_v10, plb0358_v11, plb0358_v12, plb0358_v13 Job search, Newspaper [1985-1998] plb0358_v2 Job search, Offer within Company [1985-1988] plb0358_v4 Job search, Other [1989-1998] plb0358_v7 Job search, Private Agent [1995-1998] plb0358_v9 Job search, Self Employed [1985-1998] plb0358_v6 Job search, motives (irregular) [1994-2017] plb0111 Job search, preferences (irregular) [1994-2017] plb0426 Labor Market Experience plb0737, plb0738, plb0739, plb0740, plb0741 [2022] Labor intensity [2015], [2016-2018], [2015-2018] plb0593_v1, plb0593_v2,plb0594 Leaving a job [1985-2020], [2001- 2020], [1985-2000] plb0282_h,plb0282_v1, plb0282_v2 [2022, 2023] Leaving a job, Abandonment of own business [1985-1998] plb0304_v8 Leaving a job, Closure of operations [1991-1998] plb0304_v11 Leaving a job, Compensation [1991-2020], [1991- 2001], [1991-2020], [2002-2020] plc0040,plc0041_h, plc0041_v1, plc0041_v2 [2022, 2023] Leaving a job, Early Retirement [1987-1998] plb0304_v10 Leaving a job, End Fixed-Term Contract [1985-1998] plb0304_v2 continues on next page 2.2. Work and Employment 9 SOEP Survey Paper 1261SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Table 2 – continued from previous page Questionnaire Module Years Variables Preview Leaving a job, End Vocational Training [1985-1998] plb0304_v3 Leaving a job, Exempted [1991-1998] plb0304_v12 Leaving a job, Month [1985-2020] plb0298,plb0299 [2022, 2023] Leaving a job, Months Worked [1985-2020] plb0302 [2022, 2023] Leaving a job, Mutually agreed dissolution [1985-1990] plb0304_v5 Leaving a job, Nonresponse [2004-2020] plb0300 [2022] Leaving a job, Other [1985-1998] plb0304_v9 Leaving a job, Own Resignation [1985-1998] plb0304_v4 Leaving a job, Perspective after Leaving [1999], [2000-2020] plb0305_v1, plb0305_v2 [2022, 2023] Leaving a job, Retirement [1991-1998] plb0304_v15 Leaving a job, Termination by Employer [1985-1998] plb0304_v1 Leaving a job, Transfer [1985-1998] plb0304_v7 Leaving a job, Transfer at own request [1985-1998] plb0304_v6 Leaving a job, Type of Leaving [1985-2020], [1999- 2000], [2001-2020] plb0304_h, plb0304_v13, plb0304_v14 [2022, 2023] Leaving a job, Years Worked [1985-2020] plb0301 [2022, 2023] Maternity / parental leave [1999-2000], [2001- 2020] plb0019_v1, plb0019_v2 [2022] Occupational expectations, non-employed (irregular) [1999-2020] plb0427,plb0428, plb0429 [2022] Overtime, October 2014 [2015] plb0582,plb0583 Paid breaks, October 2014 [2015] plb0575 Paid breaks, October 2015 [2015] plb0576 Paid breaks, October 2016 [2015] plb0577 Paid breaks, October 2017 [2015] plb0578 Performance evaluation by superior [2004,2008,2011,2016] plb0098,plb0099, plb0100,plb0101, plb0102 continues on next page 10 Chapter 2. Topics of SOEP-Core SOEP Survey Paper 1261SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Table 2 – continued from previous page Questionnaire Module Years Variables Preview Professional expectations [1985,1987,1989- 1994,1996,1998] plb0432_v1, plb0433_v1, plb0434_v1, plb0435_v1, plb0436_v1, plb0437_v1, plb0438_v1, plb0439_v1, plb0440_v1, plb0441_v1, plb0442_v1 Professional expectations, next two years (irregular) [1999-2018] plb0432_v2, plb0433_v2, plb0434_v2, plb0435_v2, plb0436_v2, plb0437_v2, plb0438_v2, plb0439_v2, plb0440_v2, plb0441_v2, plb0442_v2 [2023] Registered unemployed [1985-2020] plb0021 [2022, 2023] Self-employment, reasons [2010,2015] plb0333,plb0334, plb0335,plb0336, plb0337,plb0338 Short-time compensation (Kurzarbeitergeld) [1984-2001,2003- 2005,2010-2011], [1984-2001,2003-2005], [1984], [2010-2011], [1985-2001,2003- 2005,2010-2011] plc0057_h,plc0057_v1, plc0057_v2, plc0058_v1, plc0058_v2 [2022, 2023] Side jobs [1998-2007], [1998] plb0382_h,plb0382_v1 Side jobs, Agriculture [1999-2007] plb0382_v2 Side jobs, Construction [1999-2007] plb0382_v3 Side jobs, Days [1985-2016] plb0396 Side jobs, Gross Income [1995-2016], [1995- 2001], [2002-2016] plc0062_h,plc0062_v1, plc0062_v2 Side jobs, Helping Family Members out [1986-2016] plb0392 Side jobs, Hours per Month [1985-2014] plb0397 Side jobs, Hours per Week [2015-2016] plb0573 Side jobs, Industrial Sector [1999-2007] plb0382_v4 Side jobs, Iregual [1985-2016] plb0395 Side jobs, Months [2000-2013] plb0398 continues on next page 2.2. Work and Employment 11 SOEP Survey Paper 1261SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Table 2 – continued from previous page Questionnaire Module Years Variables Preview Side jobs, Occupational Classification ISCO08 [2013-2016] p_isco08_sidejob, p_isco08_sidejob1, p_isco08_sidejob2, p_isco08_sidejob3 Side jobs, Occupational Classification ISCO88 [1991-2016] p_isco88_sidejob, p_isco88_sidejob1, p_isco88_sidejob2, p_isco88_sidejob3 Side jobs, Other [1985-2016] plb0393 Side jobs, Regular [1985-2016] plb0394 Side jobs, Service Sector [1999-2007] plb0382_v5 Standby duty [2011,2014-2019] plb0212,plb0213, plb0214,plb0215 [2023] Start of working hours (irregular) [2002-2019] plb0180,plb0181, plb0182 Starting a new job, Acceptable Position [1984-2020] plb0423 [2022, 2023] Starting a new job, Active Job Search [1994-1998], [1999- 2020] plb0424_v1, plb0424_v2 [2022, 2023] Starting a new job, Desired Employment Type [1984-2020] plb0240 [2022, 2023] Starting a new job, Expected Minimum Income [1987-1989,1992- 1994,1996-2001], [2002-2020] plb0420_v1, plb0420_v2 [2022, 2023] Starting a new job, Intention [1984-1993], [1994- 2020] plb0417_v1, plb0417_v2 [2022, 2023] Starting a new job, Nonresponse Salary [1987-1989,1992- 1994,1996-2020] plb0421 [2022, 2023] Starting a new job, Number of Hours [2007-2020] plb0422 [2022, 2023] Starting a new job, Suitable Job [1987-2020], [1987- 2002], [2003-2020] plb0419_h,plb0419_v1, plb0419_v2 Starting a new job, Timing [1984-2020] plb0418 [2022, 2023] Supervisory position [2007,2009,2011,2013,2015,2017]plb0067,plb0068, plb0069 [2023] Use of professional skills in job [1985-2007,2009] plb0357 Vacation entitlement, Carried over Vaccation [2005,2010] plb0275,plb0276 Vacation entitlement, Contracted Days [2000,2005,2010] plb0269 Vacation entitlement, Days on Vaccation [1985- 1990,2000,2005,2010] plb0265 Vacation entitlement, Expired Vaccation [2005,2010] plb0273,plb0274 Vacation entitlement, Not specified [2005,2010] plb0270,plb0272 Work council (Betriebsrat) [2001,2006,2011,2016,2019]plb0050 [2022] continues on next page 12 Chapter 2. Topics of SOEP-Core SOEP Survey Paper 1261SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Table 2 – continued from previous page Questionnaire Module Years Variables Preview Work from home [1997,1999,2002,2009- 2014,2020], (irregular) [1997-2020], [2012], [2013] plb0095,plb0096_v1, plb0096_v2, plb0096_v3 Work from home, Possibility [1997,1999,2009-2014] plb0097 [2022] Work from home, Possibility in Contract [2020] plb0697 [2022] Work in black economy [2015-2016], [2015] plb0571,plb0572 Work time regulations [2003,2005,2007,2009- 2019] plb0211 [2023] Work, last 7 days [1999-2020] plb0018 [2022, 2023] Working hours, October 2014 [2015] plb0579,plb0579_h, plb0580,plb0581, plb0581_h Working overtime [1997-2020] plb0193 Working overtime, Compensation Period [2020], [2002-2020] plb0194_v1, plb0194_v2 [2022, 2023] Working overtime, Compensation period [2002-2020], [2018- 2020], [2002-2017] plb0220_h,plb0220_v1, plb0220_v2 [2022, 2023] Working overtime, Financial Compensation [2015-2020] plb0605 [2022, 2023] Working overtime, Hours Last Month [1986,1988-2020] plb0197 [2022, 2023] Working overtime, Last Month [1986,1988-2020], [1986,1988-1996], [1997-2001], [2002- 2020] plb0196_h,plb0196_v1, plb0196_v2, plb0196_v3 [2022, 2023] Working overtime, Paid Hours Last Month [2001-2020] plb0198 [2022, 2023] Working overtime, Time taken off [2013-2020] plb0483,plb0484 [2022, 2023] Workload (effort-reward imbalance), Career Prospects [2006,2011-2012,2016] plb0134,plb0135 Workload (effort-reward imbalance), Interruptions [2006,2011-2012,2016] plb0120,plb0121 Workload (effort-reward imbalance), Job at risk [2006,2011-2012,2016] plb0128,plb0129 Workload (effort-reward imbalance), Poor Career Prospects [2006,2011-2012,2016] plb0124,plb0125 Workload (effort-reward imbalance), Poor Working Conditions [2006,2011-2012,2016] plb0126,plb0127 Workload (effort-reward imbalance), Problems Sleeping [2006,2011-2012,2016] plb0117 continues on next page 2.2. Work and Employment 13 SOEP Survey Paper 1261SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Table 2 – continued from previous page Questionnaire Module Years Variables Preview Workload (effort-reward imbalance), Recognition by Superiors [2006,2011-2012,2016] plb0130,plb0131 Workload (effort-reward imbalance), Recognition for Performance [2006,2011-2012,2016] plb0132,plb0133 Workload (effort-reward imbalance), Sacrifices for Career [2006,2011-2012,2016] plb0115 Workload (effort-reward imbalance), Salary [2006,2011-2012,2016] plb0136,plb0137 Workload (effort-reward imbalance), Thinking about Work [2006,2011-2012,2016] plb0113,plb0114, plb0116 Workload (effort-reward imbalance), Time Pressure [2006,2011-2012,2016] plb0112,plb0118, plb0119 Workload (effort-reward imbalance), Work Volume [2006,2011-2012,2016] plb0122,plb0123 Youth Questionnaire Jobs and money, Employment Form [2000-2020] jl0014 [2022, 2023] Jobs and money, First Job [2000-2020] jl0017,jl0018 [2022] Jobs and money, Job Search [2006-2020] jl0386 [2022, 2023] Jobs and money, Own Earnings [2000-2020] jl0013 [2022, 2023] Jobs and money, Paid Work [2006-2020] jl0385 [2022, 2023] Jobs and money, Reason for Working [2001-2020] jl0019 [2022] Jobs and money, Savings [2000-2020], [2000- 2001], [2000-2020], [2002-2020], [2000- 2020] jl0023,jl0024_h, jl0024_v1,jl0024_v2, jl0025 [2022, 2023] Jobs and money, Unemployment [2006-2020] jl0387 [2022, 2023] 14 Chapter 2. Topics of SOEP-Core SOEP Survey Paper 1261SOEP Survey Paper 1261 SOEPcompanion, Release 2023 2.3 Income, Taxes, and Social Security The income, taxes, and social security modules collect wide-ranging financial information from earnings and spending to public benefits, pensions, inheritances, taxes, and debts. They also cover assets such as real estate and other property. Questionnaire Module Years Variables Preview Individual Questionnaire Additional questions for employed people, 13th month payment prev. year [1984-2020], [1984-2001], [1984-2020], [2002-2020] plc0042,plc0043_h, plc0043_v1, plc0043_v2 [2022, 2023] Additional questions for employed people, 14th month payment prev. year [1984-2020], [1984-2001], [1984-2020], [2002-2020] plc0044,plc0045_h, plc0045_v1, plc0045_v2 [2022, 2023] Additional questions for employed people, Christmas Bonus prev. year [1984-2020], [1984-2001], [1984-2020], [2002-2020] plc0046,plc0047_h, plc0047_v1, plc0047_v2 [2022, 2023] Additional questions for employed people, No Bonus prev. year [1984-2020] plc0054 [2022, 2023] Additional questions for employed people, Other Bonus prev. year [1984-2020], [1984-2001], [1984-2020], [2002-2020] plc0052,plc0053_h, plc0053_v1, plc0053_v2 [2022, 2023] Additional questions for employed people, Profit-sharing Bonus prev. year [1985-2020], [1985-2001], [1985-2020], [2002-2020] plc0050,plc0051_h, plc0051_v1, plc0051_v2 [2022, 2023] Additional questions for employed people, Vacation Bonus prev. year [1984-2020], [1984-2001], [1984-2020], [2002-2020] plc0048,plc0049_h, plc0049_v1, plc0049_v2 [2022, 2023] Additional questions for retirees / pensioners, Accident Insurance Retirement Pension [1986-2001,2003-2020], [1986-2001], [2003-2020] plc0243_h, plc0243_v1, plc0243_v2 [2022, 2023] continues on next page 2.3. Income, Taxes, and Social Security 15 SOEP Survey Paper 1261SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Table 3 – continued from previous page Questionnaire Module Years Variables Preview Additional questions for retirees / pensioners, Accident Insurance Widow’s Pension [1986-2001,2003-2020], [1986-2001], [2003-2020] plc0286_h, plc0286_v1, plc0286_v2 [2022, 2023] Additional questions for retirees / pensioners, Company Retirement Pension [1986-2001,2003-2020], [1986-2001], [2003-2020] plc0240_h, plc0240_v1, plc0240_v2 [2022, 2023] Additional questions for retirees / pensioners, Company Widow’s Pension [1986-2001,2003-2020], [1986-2001], [2003-2020] plc0283_h, plc0283_v1, plc0283_v2 [2022, 2023] Additional questions for retirees / pensioners, Invalid Pension non-response [2003-2020] plc0251 Additional questions for retirees / pensioners, Orphan Benefit non-response [2003-2020] plc0290 Additional questions for retirees / pensioners, Other Retirement Pensions [1986-2001,2003-2020], [1986-2001], [2003-2020] plc0249_h, plc0249_v1, plc0249_v2 [2022, 2023] Additional questions for retirees / pensioners, Other Widow’s Pensions [1986-2001,2003-2020], [1986-2001], [2003-2020] plc0288_h, plc0288_v1, plc0288_v2 [2022, 2023] Additional questions for retirees / pensioners, Private Retirement Pension [2003-2020], [2018-2020] plc0242_v1, plc0242_v2 [2022, 2023] Additional questions for retirees / pensioners, Private Widow’s Pension [2003-2020] plc0285 [2022, 2023] Additional questions for retirees / pensioners, Retirement Pension Civil Servants [1986-2001,2003-2020], [1986-2001], [2003-2020] plc0236_h, plc0236_v1, plc0236_v2 [2022, 2023] Additional questions for retirees / pensioners, Riester Pension [2015-2020] plc0516,plc0517 [2022, 2023] Additional questions for retirees / pensioners, Shareholder Company [2019] plc0572 continues on next page 16 Chapter 2. Topics of SOEP-Core SOEP Survey Paper 1261SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Table 3 – continued from previous page Questionnaire Module Years Variables Preview Additional questions for retirees / pensioners, Supplementary Pension Civil Servants [1986-2001,2003-2020], [1986-2001], [2003-2020] plc0238_h, plc0238_v1, plc0238_v2 [2022, 2023] Additional questions for retirees / pensioners, Supplementary Widow’s Pension Civil Servants [1986-2001,2003-2020], [1986-2001], [2003-2020] plc0281_h, plc0281_v1, plc0281_v2 [2022, 2023] Additional questions for retirees / pensioners, War Victims Pension [1986-2001,2003-2016], [1986-2001], [2003-2016] plc0245_h, plc0245_v1, plc0245_v2 Additional questions for retirees / pensioners, War Victims Widow’s pension [1986-2001,2003-2016], [1986-2001], [2003-2016] plc0247_h, plc0247_v1, plc0247_v2 Additional questions for retirees / pensioners, Widow’s pension [1986-2001,2003-2020], [2003-2020] plc0268_h, plc0268_v1, plc0268_v2, plc0268_v3 [2022, 2023] Additional questions for retirees / pensioners, Widow’s pension Civil Servants [1986-2001,2003-2020], [1986-2001], [2003-2020] plc0279_h, plc0279_v1, plc0279_v2 [2022, 2023] Asset balance [2002] plc0340 Asset balance, Building Loan Contract (Bausparvertrag) [2007,2012], [2017,2019], [2007,2012], [2017,2019] plc0317_v1, plc0317_v2, plc0318_v1, plc0318_v2 Asset balance, Building Society Savings [2007,2012,2017,2019] plc0315,plc0316, plc0319 Asset balance, Cash Surrender [2002] plc0327,plc0335, plc0336,plc0337, plc0338 Asset balance, Enterprise [2002] plc0341,plc0364, plc0365,plc0366, plc0367,plc0368, plc0369 Asset balance, Financial Assets [2002], [2002,2007,2012], [2002,2007,2012,2017,2019] plc0314,plc0326, plc0328,plc0329, plc0330,plc0331, plc0332,plc0333, plc0334 Asset balance, Financial Burden [2002], [2002,2007,2012] plc0408,plc0409, plc0411,plc0412, plc0413,plc0414, plc0415,plc0416, plc0417,plc0418, plc0419,plc0420 continues on next page 2.3. Income, Taxes, and Social Security 17 SOEP Survey Paper 1261SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Table 3 – continued from previous page Questionnaire Module Years Variables Preview Riester / Ruerup pension plans (irregular) [2004-2020] plc0313_h, plc0313_v1, plc0313_v2,plc0430, plc0431 [2022] Social security, Don’t know [2002,2007,2012,2017] plc0009 Social security, Financial Security [2002,2007,2012,2017] plc0111,plc0112, plc0113,plc0114 [2022] Social security, Minimum Household Income [1992,2002,2007,2012,2017], [1992], [2002,2007,2012,2017] plc0001_h, plc0001_v1, plc0001_v2 [2022] Wage tax classification [1991,1993,2004,2016], [2004,2016] plc0091_h, plc0091_v1, plc0091_v2, plc0091_v3, plc0091_v4, plc0091_v5, plc0091_v6, plc0091_v7, plc0091_v8, plc0091_v9 Household Questionnaire Alimony [2010] hlc0091,hlc0092, hld0004,hld0005 Credit burden [2005-2016], [2005-2011], [2011-2016] hlc0115_h, hlc0115_v1, hlc0115_v2 Expenditures on Food, Month (irregular) [1998-2020], [1998,2000-2001], (irregular) [2003-2020] hlf0436_h, hlf0436_v1, hlf0436_v2 [2022, 2023] Expenditures on Food, Week (irregular) [1998-2020], [1998,2000-2001], (irregular) [2003-2020] hlf0435_h, hlf0435_v1, hlf0435_v2 [2022, 2023] Good/Low Income, Good Household Income [1992,1997,2007,2018], [1992,1997], [2007,2018] hlc0022_h, hlc0022_v1, hlc0022_v2 Good/Low Income, Insufficient Household Income [1992,1997,2007,2018], [1992,1997], [2007,2018] hlc0020_h, hlc0020_v1, hlc0020_v2 Good/Low Income, Just Sufficient Household Income [1992,1997,2007,2018], [1992,1997], [2007,2018] hlc0021_h, hlc0021_v1, hlc0021_v2 Good/Low Income, Poor Household Income [1992,1997,2007,2018], [1992,1997], [2007,2018] hlc0019_h, hlc0019_v1, hlc0019_v2 Good/Low Income, Very Good Household Income [1992,1997,2007,2018], [1992,1997], [2007,2018] hlc0023_h, hlc0023_v1, hlc0023_v2 Good/Low Income, Very Poor Household Income [1992,1997,2007,2018], [1992,1997], [2007,2018] hlc0018_h, hlc0018_v1, hlc0018_v2 continues on next page 24 Chapter 2. Topics of SOEP-Core SOEP Survey Paper 1261SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Table 3 – continued from previous page Questionnaire Module Years Variables Preview Household income / expenses, Basic financial security in old age prev. Year [2005-2020] hlc0061_h, hlc0061_v1, hlc0061_v2,hlc0062, hlc0063,hlc0071 [2022, 2023] Household income / expenses, Child Allowance prev. year [1984-2000], [1985-2020], [1985-2001], [2001-2020], [2002-2020] hlc0040,hlc0041, hlc0042_h, hlc0042_v1, hlc0042_v2 [2022, 2023] Household income / expenses, Child Allowance today [1995-2020], [2000-2009], [1995-2020], [1995-2001], [2010-2020], [2002-2020] hlc0044_h, hlc0044_v1, hlc0044_v2, hlc0045_h, hlc0045_v1, hlc0045_v2 [2022, 2023] Household income / expenses, Child Benifit [1984-2020], [1985-1990], [1991-1995], [1996-2020] hlc0039_h, hlc0039_v1, hlc0039_v2, hlc0039_v3 [2022, 2023] Household income / expenses, Child Care Subsidy [2009-2020], [2011-2020], [2010-2020] hlc0046_h, hlc0046_v1, hlc0046_v2, hlc0046_v3, hlc0046_v4, hlc0047_h, hlc0047_v1, hlc0047_v2,hlc0124, hlc0125 [2022, 2023] Household income / expenses, Child Care Subsidy prev. Year [2009-2020] hlc0049_h, hlc0049_v1, hlc0049_v2, hlc0050_h, hlc0050_v1, hlc0050_v2, hlc0051_h, hlc0051_v1, hlc0051_v2,hlc0121, hlc0122,hlc0123 [2022, 2023] Household income / expenses, Compulsory Long Term Care Insurance [2001-2020], [2001], [1996- 2020], [1996], [1997-1999], [1997-2000], [1997-1998], [2000-2009], [1996-2020], [1996-2001], [2002-2020], [2010-2020], [2002-2020] hlc0079_h, hlc0079_v1, hlc0079_v2, hlc0085_h, hlc0085_v1, hlc0085_v2, hlc0085_v3, hlc0085_v4, hlc0085_v5, hlc0085_v6, hlc0090_h, hlc0090_v1, hlc0090_v2 [2022, 2023] continues on next page 2.3. Income, Taxes, and Social Security 25 SOEP Survey Paper 1261SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Table 3 – continued from previous page Questionnaire Module Years Variables Preview Household income / expenses, Compulsory Long Term Care Insurance prev. Year [2001-2020] hlc0078 [2022, 2023] Household income / expenses, Family Members Support [2001-2020] hlc0077 [2022, 2023] Household income / expenses, Help with living costs [1984-2009], [1984,1991- 2009], [1985-1990], [1995-2020], [1999-2009], [1995-2020], [1995-2001], [1995-1998,2010-2020], [2002-2020] hlc0066_h, hlc0066_v1, hlc0066_v2, hlc0067_h, hlc0067_v1, hlc0067_v2, hlc0068_h, hlc0068_v1, hlc0068_v2 [2022, 2023] Household income / expenses, Help with living costs prev. Year [1984-2020], [1985- 1990], [1992-2009], [1984-1991,2001-2020], [1984-1991,2001], [1984,1991,2010-2020], [2002-2020] hlc0055_h, hlc0055_v1, hlc0055_v2, hlc0055_v3, hlc0059_h, hlc0059_v1, hlc0059_v2 [2022, 2023] Household income / expenses, Housing assistance [1984-2020], [1985-1990], [1995-2020], [1999-2009], [1995-2020], [1995-2001], [1984,1991-2020], [1995- 1998,2010-2020], [2002- 2020] hlc0080_h, hlc0080_v1, hlc0080_v2, hlc0083_h, hlc0083_v1, hlc0083_v2, hlc0084_h, hlc0084_v1, hlc0084_v2 [2022, 2023] Household income / expenses, Housing assistance prev. Year [1984-2020], [1984-2001], [1984-2020], [2002-2020] hlc0081,hlc0082_h, hlc0082_v1, hlc0082_v2 [2022, 2023] Household income / expenses, Income Bracket [1999-2000], [2001-2002], [2003-2020] hlc0006_v1, hlc0006_v2, hlc0006_v3 Household income / expenses, Monthly Household Income [1984-2020], [1984-2001], [2002-2020] hlc0005_h, hlc0005_v1, hlc0005_v2 [2022, 2023] Household income / expenses, Reduction of earning capacity [2005-2020], [2005-2009], [2010-2020] hlc0070_h, hlc0070_v1, hlc0070_v2 [2022, 2023] continues on next page 26 Chapter 2. Topics of SOEP-Core SOEP Survey Paper 1261SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Table 3 – continued from previous page Questionnaire Module Years Variables Preview Household income / expenses, Special Circumstances Assistance [1984-2009], [1984- 1991,2001], [2002-2009] hlc0056_h, hlc0056_v1, hlc0056_v2, hlc0056_v3, hlc0058,hlc0060_h, hlc0060_v1, hlc0060_v2 Household income / expenses, Subsistence Support prev. year [1984-1991,2001-2020] hlc0057 [2022, 2023] Household income / expenses, Unemplyoment Subsidy II [2005-2020], [2005-2009], [2010-2020], [2005-2020] hlc0064_h, hlc0064_v1, hlc0064_v2,hlc0065 [2022, 2023] Household income / expenses, Unemplyoment Subsidy II prev. year [2006-2020] hlc0052,hlc0053, hlc0054 [2022, 2023] Household income / expenses; Number of Children [1995-2020] hlc0043 [2022, 2023] Income and expenses from renting / leasing [1984-1990,1992-2020], [1984-1990,1992-2001], [1984-1990,1992-2020], [2002-2020] hlc0007,hlc0008_h, hlc0008_v1, hlc0008_v2 [2022, 2023] Income and expenses from renting / leasing, Maintenance costs [1984-1990,1992-2020], [1984-1990,1992-2001], [2002-2020], [2016-2020] hlc0111_h, hlc0111_v1, hlc0111_v2,hlc0176 [2022, 2023] Income and expenses from renting / leasing, Redemption payments [1985-1990,1992-2020], [1985-1990,1992-2001], [2002-2020], [2016-2020] hlc0112_h, hlc0112_v1, hlc0112_v2,hlc0177 [2022, 2023] Income and expenses from renting / leasing, Tax Deduction [2005-2020] hlc0009,hlc0010 [2022, 2023] Inheritance, present, lottery prize [2016-2020] hlc0178,hlc0179, hlc0180,hlc0181, hlc0182,hlc0183 [2022, 2023] Investments, Building Society [1984-2020] hlc0105 [2022, 2023] Investments, Combined Savings [1990] hlc0097 Investments, Fixed Interest Securities [1984-2020] hlc0107 [2022, 2023] Investments, Interest and Dividend Income [1984-2001], [2002-2020], [1985-2020] hlc0013_v1, hlc0013_v2,hlc0014 [2022, 2023] Investments, Life Insurance [1984-2020] hlc0106 [2022, 2023] Investments, No Securities [1984-2020] hlc0093 [2022, 2023] continues on next page 2.3. Income, Taxes, and Social Security 27 SOEP Survey Paper 1261SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Table 3 – continued from previous page Questionnaire Module Years Variables Preview Investments, Nonresponse [2003-2020], [2016-2020] hlc0096,hlc0184 [2022, 2023] Investments, Operating Assets [1984-2020] hlc0104 [2022, 2023] Investments, Other Securities [2001-2020] hlc0108 [2022, 2023] Investments, Savings Account [1984-2020] hlc0098 [2022, 2023] Investments, Taxdeductible Loan [2005-2020] hlc0094,hlc0095 [2022, 2023] Ratio between income and expenditures [2010-2013], [2016-2018], [2010] hlc0024_v1, hlc0024_v2,hlc0030 Ratio between income and expenditures, Cap shortfall [2010-2013], [2016] hlc0029_v1, hlc0029_v2 Ratio between income and expenditures, Expenditure Surplus [2010-2013], [2016-2018], [2010-2013], [2016] hlc0027_v1, hlc0027_v2, hlc0028_v1, hlc0028_v2 Ratio between income and expenditures, Income Surplus [2010-2013], [2016-2018], [2010-2013], [2016] hlc0025_v1, hlc0025_v2, hlc0026_v1, hlc0026_v2 Repayments of loans [1997-2020], [1997-2011], [1997-2020], [1997-2001], [2002-2011], [2011-2020] hlc0113_h, hlc0113_v1, hlc0113_v2, hlc0114_h, hlc0114_v1, hlc0114_v2 [2022, 2023] Savings [1992-2020], [2002-2014] hlc0119_h, hlc0119_v1, hlc0119_v2, hlc0119_v3, hlc0119_v4, hlc0120_h, hlc0120_v1, hlc0120_v2, hlc0120_v3, hlc0120_v4 [2022, 2023] Savings, Deficit [2021] hfehlb1,hfehlb2, hfehlb3,hfehlb4, hfehlb5,hfehlb6, hfehlb7 28 Chapter 2. Topics of SOEP-Core SOEP Survey Paper 1261SOEP Survey Paper 1261 SOEPcompanion, Release 2023 2.4 Family and Social Networks As a household study, the SOEP offers rich information on family and social relationships and how these connections change in different stages of life. The modules dealing with family and social networks cover the entire life cycle beginning with pregnancy and childbirth and continuing through parenthood, family formation, friendships, marriage, divorce, and death, and also provide a wealth of additional information on important life events. Questionnaire Module Years Variables Preview Individual Questionnaire Childcare [2003-2020] suppartn Circle of friends, sociodemographics [2011,2016] pld0104,pld0105,pld0106 Circle of friends, sociodemographics, age [2006,2011- 2012,2016] pld0095,pld0096,pld0097 Circle of friends, sociodemographics, education [2006,2011,2016] pld0101,pld0102,pld0103 Circle of friends, sociodemographics, labor force status [2006,2011- 2012,2016] pld0098,pld0099,pld0100 Circle of friends, sociodemographics, relations (unregelmaessig) [1988-2016], [2012] pld0089_h,pld0089_v1, pld0089_v2,pld0090_h, pld0090_v1,pld0090_v2, pld0091_h,pld0091_v1, pld0091_v2 Circle of friends, sociodemographics, sex (unregelmaessig) [1988-2016] pld0092,pld0093,pld0094 Circle of friends, trustworthy persons [2006,2011,2016,2019], [2006,2011,2016,2019], (unregelmaessig) [1991-2019], [1991,1996], [2001], [2006,2011,2016,2019], (unregelmaessig) [1991-2019], [1991,1996], [2001], [2006,2011,2016,2019] pld0062_v1,pld0063_v1, pld0064_v1,pld0065_v1, pld0066_v1,pld0067, pld0068_v1,pld0069_v1, pld0070_v1,pld0071_v1, pld0072_v1,pld0073, plf0049_h,plf0049_v1, plf0049_v2,plf0049_v3, plf0050_h,plf0050_v1, plf0050_v2,plf0050_v3 continues on next page 2.4. Family and Social Networks 29 SOEP Survey Paper 1261SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Table 4 – continued from previous page Questionnaire Module Years Variables Preview Circle of friends, trustworthy persons (M3-M5) [2017-2019] pld0062_v2,pld0063_v2, pld0064_v2,pld0065_v2, pld0066_v2,pld0068_v2, pld0069_v2,pld0070_v2, pld0071_v2,pld0072_v2 Circle of friends, trustworthy persons: conflicts [2006,2011,2013,2016,2019], [2006,2011,2013,2016,2019] pld0077,pld0078,pld0079, pld0080,pld0081,pld0082 Circle of friends, trustworthy persons: help [2006,2011,2016,2019]pld0074,pld0075,pld0076 Circle of friends, trustworthy persons: unpleasant truths [2006,2011,2016,2019], [2017-2018], [2006,2011,2016,2019], [2017-2018], [2006,2011,2016,2019], [2017-2018], [2011,2016,2019], [2017-2018], [2011,2016,2019], [2017-2018], [2006,2011,2016,2019] pld0083_v1,pld0083_v2, pld0084_v1,pld0084_v2, pld0085_v1,pld0085_v2, pld0086_v1,pld0086_v2, pld0087_v1,pld0087_v2, pld0088 Family changes [1991,1996,2001], [1985-2020], [2003-2020] pld0012,pld0013,pld0014, pld0038,pld0039,pld0040, pld0159,pld0160 [2022, 2023] Family changes, childbirth [1999-2020] pld0152,pld0153,pld0154 [2022, 2023] Family changes, death [1999-2020] pld0146,pld0147,pld0148, pld0161,pld0162,pld0163, pld0164,pld0165,pld0166, pld0167,pld0168,pld0169, pld0170,pld0171 [2022, 2023] Family changes, divorce [1999-2020] pld0140,pld0141,pld0142 [2022, 2023] Family changes, marriage [1999-2020] pld0134,pld0135,pld0136 [2022, 2023] Family changes, moving in [1999-2020] pld0137,pld0138,pld0139 [2022, 2023] Family changes, moving out (child) [1999-2020] pld0149,pld0150,pld0151 [2022, 2023] Family changes, other [1985- 1995,1999- 2008,2010-2020] pld0155,pld0156,pld0158 [2022, 2023] Family changes, separation [1999-2020] pld0143,pld0144,pld0145 [2022, 2023] Family network, aunt [2006,2011,2016] pld0115,pld0116 Family network, children [1991,1996,2001,2006,2011,2016]pld0025,pld0026,pld0027, pld0028,pld0301i01, pld0301i02,pld0302 continues on next page 30 Chapter 2. Topics of SOEP-Core SOEP Survey Paper 1261SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Table 4 – continued from previous page Questionnaire Module Years Variables Preview Family network, distance [1991], [1991], [1996,2001], [2006,2011,2016] plj0117_v1,plj0117_v2, plj0117_v3,plj0118_v1, plj0118_v2,plj0118_v3, plj0119_v1,plj0119_v2, plj0119_v3,plj0120,plj0121, plj0122_v1,plj0122_v2, plj0122_v3,plj0123_v1, plj0123_v2,plj0123_v3, plj0124_h,plj0124_v1, plj0124_v2,plj0124_v3, plj0125_v1,plj0125_v2, plj0125_v3,plj0126, plj0127_v1,plj0127_v2, plj0127_v3,plj0128,plj0129, plj0130_v1,plj0130_v2, plj0130_v3 Family network, grandchildren [1991,1996,2001,2006,2011,2016]pld0033,pld0034 Family network, grandparents [2006,2011,2016] pld0110,pld0111,pld0112, pld0113,pld0114 Family network, other relatives [1991,1996,2001,2006,2011,2016]pld0035,pld0036 Family network, parents [1991,1996,2001,2006,2011,2016]pld0023,pld0024 Family network, siblings [1991,1996,2001,2006,2011,2016], [1991,1996,2001,2003,2006,2011], [1991,1996,2001,2006,2011,2016], (unregelmaessig) [1991-2016] pld0029,pld0030,pld0031, pld0032 Family network, spouse [1996,2001,2006,2011,2016], [1996,2001], [2006,2011,2016], [1991,1996,2001,2006,2011,2016], [2006,2011,2016] pld0020,pld0021_h, pld0021_v1,pld0021_v2, pld0022,pld0107 Family network, stepparents [2006,2011,2016] pld0108,pld0109 Family network, uncle [2006,2011,2016] pld0117,pld0118 Friends (unregelmaessig) [2003-2020] pld0047 [2022] Leisure and activities (with child) [2003-2020] tvhrs,tvyn Marital / partnership status [2019] pld0131_v2,pld0131_v3, pld0132_v1,pld0132_v2, pld0133,pld0299,pld0300, plk0001_v2,plk0001_v3 [2022] Pregnancy and childbirth [2003-2020] pregplan Sexual orientation [2016] pld0298_v1,pld0298_v2, pld0298_v3 Youth Questionnaire Allowance (Pocket money) [2000-2020], [2002-2020] ,jl0022_h,jl0022_v2 [2022, 2023] continues on next page 2.4. Family and Social Networks 31 SOEP Survey Paper 1261SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Table 4 – continued from previous page Questionnaire Module Years Variables Preview Allowance (Pocket money, Deutschmark) [2000-2001] jl0021_v1,jl0022_v1 Childhood and parental home [2000-2018] jl0273,jl0279 Childhood and parental home (parent’s education, ISCO-08) [2013-2018] j_isco08_jobfather, j_isco08_jobmother Childhood and parental home (parent’s education, ISCO-88) [2000-2017] j_isco88_jobfather, j_isco88_jobmother Childhood and parental home (parent’s education, KldB 2010) [2013-2018] j_kldb2010_jobfather, j_kldb2010_jobmother Childhood and parental home (parent’s education, KldB 92) [2000-2017] j_kldb92_jobfather, j_kldb92_jobmother Childhood and parental home, father [2000-2018], [2014-2017], [2014-2018], [2015-2018] jl0307,jl0309,jl0311, jl0313_v1,jl0313_v2, jl0315,jl0327_h,jl0327_v1, jl0327_v2,jl0506,jl0508, jl0510,jl0512,jl0514,jl0516, jl0518,jl0520,jl0522 Childhood and parental home, mother [2000-2018], [2014-2017], [2014-2018], [2015-2018] jl0304,jl0308,jl0310, jl0312,jl0314_v1,jl0314_v2, jl0316,jl0328_h,jl0328_v1, jl0328_v2,jl0507,jl0509, jl0511,jl0513,jl0515,jl0517, jl0519,jl0521,jl0523 Childhood and parental home, siblings [2004-2012] jl0274,jl0275,jl0276,jl0277, jl0278,jl0446,jl0447,jl0454, jl0455,jl0456,jl0457,jl0458, jl0459,jl0460,jl0461,jl0462, jl0463,jl0464,jl0465,jl0466, jl0467,jl0468,jl0469,jl0470, jl0471,jl0472,jl0473,jl0474, jl0475,jl0476,jl0477,jl0478, jl0479,jl0480,jl0481,jl0482, jl0483,jl0484,jl0485,jl0486, jl0487,jl0488,jl0489,jl0490, jl0491,jl0492,jl0493,jl0494, jl0495,jl1406,jl1407,jl1408, jl1409,jl1410,jl1411 Parental interest in child’s performance in school [2000-2018] jl0167,jl0168,jl0169,jl0170, jl0171,jl0172,jl0173,jl0174 [2022] continues on next page 32 Chapter 2. Topics of SOEP-Core SOEP Survey Paper 1261SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Table 4 – continued from previous page Questionnaire Module Years Variables Preview Relationship to family members [2001-2018] jl0026,jl0027,jl0028,jl0029, jl0030,jl0031,jl0032,jl0033, jl0034,jl0040,jl0041,jl0043, jl0044,jl0045,jl0046,jl0047, jl0048,jl0049,jl0050,jl0051, jl0052,jl0053,jl0054,jl0055, jl1043 [2022] Relationship to family members, conflicts [2001-2018] jl0035_v1,jl0035_v2, jl0036_v1,jl0036_v2, jl0037_v1,jl0037_v2, jl0038_v1,jl0038_v2, jl0039_v1,jl0039_v2 [2022] Household Questionnaire Care Provision, Outside Household hle0025, hle0039, hle0040, hle0041, hle0042, hle0043, hle0044, hle0026, hle0027, hle0028, hle0029, hle0030, hle0031, hle0032, hle0033, hle0034, hle0035, hle0036, hle0037, hle0038 [2022] Child, COVID-19 kd_covquat_1, kc_covimpf_1, kd_covquat_2, kc_covimpf_2, kd_covquat_3, kc_covimpf_3, hcovidquat_4, hcovimpf1_4, kd_covquat_5, kc_covimpf_5, kd_covquat_6, kc_covimpf_6, kd_covquat_7, kc_covimpf_7, kd_covquat_8, kc_covimpf_8, kd_covquat_9, kc_covimpf_9, kd_covquat_10, kc_covimpf_10 [2022] continues on next page 2.4. Family and Social Networks 33 SOEP Survey Paper 1261SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Table 5 – continued from previous page Questionnaire Module Years Variables Preview Household Questionnaire COVID-19, Positive Test Result [2021] hkcovarzta_1, hkmnt1a_1, hkmnt2a_1, hkmnt3a_1, hkmnt4a_1,hkcovkra_1,hkcovstesta_1,hkcovarztb_2,hkmnt1b_2, hkmnt2b_2, hkmnt3b_2, hkmnt4b_2,hkcovkrb_2,hkcovstestb_2,hkcovarztc_3,hkmnt1c_3, hkmnt2c_3, hkmnt3c_3, hkmnt4c_3,hkcovkrc_3,hkcovstestc_3,hkcovarztd_4,hkmnt1d_4, hkmnt2d_4, hkmnt3d_4, hkmnt4d_4,hkcovkrd_4,hkcovstestd_4,hkcovarzte_5,hkmnt1e_5, hkmnt2e_5, hkmnt3e_5, hkmnt4e_5,hkcovkre_5,hkcovsteste_5,hkcovarztf_6,hkmnt1f_6, hkmnt2f_6, hkmnt3f_6, hkmnt4f_6,hkcovkrf_6,hkcovstestf_6,hkcovarztg_7,hkmnt1g_7, hkmnt2g_7, hkmnt3g_7, hkmnt4g_7,hkcovkrg_7,hkcovstestg_7,hkcovarzth_8,hkmnt1h_8, hkmnt2h_8, hkmnt3h_8, hkmnt4h_8,hkcovkrh_8,hkcovstesth_8,hkcovarzti_9,hkmnt1i_9, hkmnt2i_9, hkmnt3i_9, hkmnt4i_9,hkcovkri_9,hkcovstesti_9, hkcovarztj_10, hkmnt1j_10, hkmnt2j_10, hkmnt3j_10, hkmnt4j_10,hkcovkrj_10,hkcovstestj_10 continues on next page 40 Chapter 2. Topics of SOEP-Core SOEP Survey Paper 1261SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Table 5 – continued from previous page Questionnaire Module Years Variables Preview COVID-19, Quarantine [2021] hkcovhqa_1,hkcovhqb1a_1,hkcovhqb2a_1,hkcovhqb_2, hkcovhqb1b_2,hkcovhqb2b_2,hkcovhqc_3, hkcovhqb1c_3,hkcovhqb2c_3,hkcovhqd_4, hkcovhqb1d_4,hkcovhqb2d_4,hkcovhqe_5, hkcovhqb1e_5,hkcovhqb2e_5,hkcovhqf_6, hkcovhqb1f_6,hkcovhqb2f_6,hkcovhqg_7, hkcovhqb1g_7,hkcovhqb2g_7,hkcovhqh_8, hkcovhqb1h_8,hkcovhqb2h_8,hkcovhqi_9, hkcovhqb1i_9,hkcovhqb2i_9,hkcovhqi_10, hkcovhqb1j_10,hkcovhqb2j_10 continues on next page 2.5. Health and Care 41 SOEP Survey Paper 1261SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Table 5 – continued from previous page Questionnaire Module Years Variables Preview COVID-19, Symptoms [2021] hktmntgr1a_1,hktmntgr2a_1,hktmntgr3a_1,hktmntgr4a_1,hktmntgr5a_1,hktmntgr6a_1,hktmntgr7a_1,hktmntgr8a_1,hkcovsb1_1, hkcovsb2_1,hkcovsb3_1,hkcovse1_1, hkcovse2_1,hkcovse3_1,hktmntgr1b_2,hktmntgr2b_2,hktmntgr3b_2,hktmntgr4b_2,hktmntgr5b_2,hktmntgr6b_2,hktmntgr7b_2,hktmntgr8b_2,hkcovsb1b_2,hkcovsb2b_2,hkcovsb3b_2,hkcovse1b_2,hkcovse2b_2,hkcovse3b_2,hktmntgr1c_3,hktmntgr2c_3,hktmntgr3c_3,hktmntgr4c_3,hktmntgr5c_3,hktmntgr6c_3,hktmntgr7c_3,hktmntgr8c_3,hkcovsb1_3, hkcovsb2_3,hkcovsb3_3,hkcovse1_3, hkcovse2_3,hkcovse3_3,hktmntgr1d_4,hktmntgr2d_4,hktmntgr3d_4,hktmntgr4d_4,hktmntgr5d_4,hktmntgr6d_4,hktmntgr7d_4,hktmntgr8d_4,hkcovsb1_4, hkcovsb2_4,hkcovsb3_4,hkcovse1_4, hkcovse2_4,hkcovse3_4,hktmntgr1e_5,hktmntgr2e_5,hktmntgr3e_5,hktmntgr4e_5,hktmntgr5e_5,hktmntgr6e_5,hktmntgr7e_5,hktmntgr8e_5,hkcovsb1_5, hkcovsb2e_5,hkcovsb3e_5,hkcovse1e_5,hkcovse2e_5,hkcovse3e_5,hktmntgr1f_6,hktmntgr2f_6,hktmntgr3f_6,hktmntgr4f_6,hktmntgr5f_6,hktmntgr6f_6,hktmntgr7f_6,hktmntgr8f_6,hkcovsb1f_6,hkcovsb2f_6,hkcovsb3f_6,hkcovse1f_6,hkcovse2f_6,hkcovse3f_6,hktmntgr1g_7,hktmntgr2g_7,hktmntgr3g_7,hktmntgr4g_7,hktmntgr5g_7,hktmntgr6g_7,hktmntgr7g_7,hktmntgr8g_7,hkcovsb1g_7,hkcovsb2g_7,hkcovsb3g_7,hkcovse1g_7,hkcovse2g_7,hkcovse3g_7,hktmntgr1h_8,hktmntgr2h_8,hktmntgr3h_8,hktmntgr4h_8,hktmntgr5h_8,hktmntgr6h_8,hktmntgr7h_8,hktmntgr8h_8,hkcovsb1h_8,hkcovsb2h_8,hkcovsb3h_8,hkcovse1h_8,hkcovse2h_8,hkcovse3h_8,hktmntgr1i_9,hktmntgr2i_9,hktmntgr3i_9,hktmntgr4i_9,hktmntgr5i_9,hktmntgr6i_9,hktmntgr7i_9,hktmntgr8i_9,hkcovsb1i_9, hkcovsb2i_9,hkcovsb3i_9,hkcovse1i_9,hkcovse2i_9,hkcovse3i_9,hktmntgr1j_10,hktmntgr2j_10,hktmntgr3j_10,hktmntgr4j_10,hktmntgr5j_10,hktmntgr6j_10,hktmntgr7j_10,hktmntgr8j_10,hkcovsb1j_10,hkcovsb2j_10,hkcovsb3j_10,hkcovse1j_10,hkcovse2j_10,hkcovse3j_10 continues on next page 42 Chapter 2. Topics of SOEP-Core SOEP Survey Paper 1261SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Table 5 – continued from previous page Questionnaire Module Years Variables Preview Satisfaction with availability of care [1997,2002,2008] hlf0318 Mother and Child Instruments Health of child [2003-2020] chhealth,lstmedex, medaid3mb Health of child, disorders [2003-2020] disord,disord1,disord2,disord3,disord4, disord5,disord6,disord7,disord8,disord9 Health of child, hospital stays [2003-2020] hospital12m,hospital3mb Health of child, illnesses [2003-2020] ill0,ill10,ill11,ill12, ill13,ill14,ill15,ill2, ill31,ill32,ill4,ill5, ill6,ill7,ill8,ill9,illno Height and weight of child [2003-2020] height,weight, weightb Physical and mental health of mother [2003-2020] feeling1,feeling2,feeling3,feeling4 2.6 Home, Amenities, and Contributions of Private HH The housing, amenities, and household expenses modules provide wide-ranging information on everyday life including the type of dwelling and whether it is a rental property or owner-occupied; expenditures on personal hygiene, transportation, and vacations; and the division of household labor. Questionnaire Module Years Variables Preview Household Questionnaire Childcare costs [2010- 2013,2015,2017- 2019], [2010-2012], [2013,2015,2017- 2019] ks_cost_h,ks_cost_v1, ks_cost_v2 continues on next page 2.6. Home, Amenities, and Contributions of Private HH 43 SOEP Survey Paper 1261SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Table 6 – continued from previous page Questionnaire Module Years Variables Preview Childcare provider [1987,1995,1997,2002,2005,2007], [2002,2005,2007] kd_insta_h,kd_insta_v1, kd_insta_v2,kd_insta_v3, kd_insta_v4,kd_insta_v5, kd_insta_v6,kd_insta_v7 Childcare situation [1987,1997,1999- 2002,2004-2020] kc_care_h,kc_care_v1, kc_care_v2,kc_care_v3, kc_care_v4,kc_care_v5, kc_care_v6,kc_care_v7 Dependence on childcare hours [2002] kd_rely Leisure activities, children (unregelmaessig) [2006-2020] ka06_art,ka06_mus, ka06_non,ka06_oth, ka06_spo,ka16_art, ka16_ctr,ka16_mus, ka16_non,ka16_org, ka16_sar,ka16_smu, ka16_sot,ka16_spo, ka16_ssp,ka16_sth, ka16_yth Leisure costs, children (unregelmaessig) [2002-2019], [2002,2005,2007], [2010- 2013,2015,2017,2019], [2017-2018], [2010- 2013,2015,2017- 2019], [2010-2012], [2013,2015,2017,2019], [2017-2018] kk_amtp_h,kk_amtp_v1, kk_amtp_v2,kk_amtp_v3, kk_cost_h,kk_cost_v1, kk_cost_v2,kk_cost_v3 Lunch, childcare (unregelmaessig) [1997-2019], [1997,2002,2005,2007], [2010- 2013,2015,2017,2019], [2017-2018] kd_lunch_h,kd_lunch_v1, kd_lunch_v2,kd_lunch_v3 Lunch, school [2010- 2013,2015,2017-2019] ks_lunch School attendance by child [1984-2020], [1984- 1994], [1990], [1991], [1995-2020], [2017], [2016-2019] ks_gen_h,ks_gen_v1, ks_gen_v2,ks_gen_v3, ks_gen_v4,ks_gen_v5, ks_spe School provider and costs (unregelmaessig) [1987-2019], [1987,1996], [2010-2012], [2013,2015,2017- 2019] ks_amtp_h,ks_amtp_v1, ks_amtp_v2,ks_amtp_v3 continues on next page 44 Chapter 2. Topics of SOEP-Core SOEP Survey Paper 1261SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Table 6 – continued from previous page Questionnaire Module Years Variables Preview Household Questionnaire Change in residential situation [1991-2020], [1991- 1998], [2015-2020], [1991-2020], [1999- 2020] hlf0106,hlf0107_h, hlf0107_v1,hlf0107_v2, hlf0523 [2022, 2023] Changes in home fixtures and furnishings since last year [2004,2006,2008,2010- 2013] hlc0116,hlc0117,hlf0159, hlf0164,hlf0165_h, hlf0165_v1,hlf0165_v2, hlf0166,hlf0167,hlf0223, hlf0224,hlf0225,hlf0226, hlf0227,hlf0228,hlf0229, hlf0230,hlf0231,hlf0232, hlf0233,hlf0234,hlf0235, hlf0236,hlf0237,hlf0238, hlf0244,hlf0245,hlf0246, hlf0247,hlf0248,hlf0249, hlf0250,hlf0251,hlf0252 Changes in home fixtures and furnishings since last year: Internet [2000,2002,2004,2006], (unregelmaessig) [2000-2013], [2000], [2002,2004,2006,2008,2010- 2013] hlf0169_v1,hlf0169_v2, hlf0169_v3,hlf0169_v4, hlf0169_v5,hlf0169_v6, hlf0169_v7,hlf0170_h, hlf0170_v1,hlf0170_v2 Changes in home fixtures and furnishings since last year: car (unregelmaessig) [2000-2013], [2000], [2002,2004,2006,2008,2010- 2013], [2010-2013], [2010-2011] hlf0209_h,hlf0209_v1, hlf0209_v2,hlf0210,hlf0211 Changes in home fixtures and furnishings since last year: cell phone (unregelmaessig) [2000-2020], (unregelmaessig) [2000-2013], [2010-2013] hlf0241_h,hlf0241_v1, hlf0241_v2,hlf0241_v3, hlf0241_v4,hlf0241_v5, hlf0241_v6,hlf0241_v7, hlf0241_v8,hlf0242,hlf0243 Changes in home fixtures and furnishings since last year: kitchen appliances (unregelmaessig) [1998-2013] hlf0214,hlf0215,hlf0216, hlf0217,hlf0218,hlf0219, hlf0220,hlf0221,hlf0222 Changes in home fixtures and furnishings since last year: motorcycle, moped (unregelmaessig) [2000-2013], [2000], [2002,2004,2006,2008,2010- 2013], [2010-2013] hlf0212_h,hlf0212_v1, hlf0212_v2,hlf0213 Changes in home fixtures and furnishings since last year: phone (unregelmaessig) [1990-2020], (unregelmaessig) [1990-2013], [2013,2015], [2014], [2016-2020], (unregelmaessig) [1998-2013] hlf0239_h,hlf0239_v1, hlf0239_v2,hlf0239_v3, hlf0239_v4,hlf0240 Cleaning or household help [1991,1994,1999- 2020], [2010-2020] hlf0261,hlf0262 [2022, 2023] Comparison of old and new home [1985- 2013,2015,2017,2019- 2020] hlf0126,hlf0127,hlf0128, hlf0129,hlf0130,hlf0131, hlf0132 [2022, 2023] continues on next page 2.6. Home, Amenities, and Contributions of Private HH 45 SOEP Survey Paper 1261SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Table 6 – continued from previous page Questionnaire Module Years Variables Preview Consumption Module [2010-2013] hlf0172 Consumption Module: Cars (unregelmaessig) [1990-2013], [1991] hlf0163_h,hlf0163_v1, hlf0163_v2 Consumption Module: Clothes and Shoes [2010-2013] hlf0379,hlf0380,hlf0381, hlf0382 [2023] Consumption Module: Cosmetics [2010-2013] hlf0383,hlf0384,hlf0385, hlf0386 Consumption Module: Culture [2010-2013] hlf0399,hlf0400,hlf0401, hlf0402 Consumption Module: Education [2010-2013] hlf0395,hlf0396,hlf0397, hlf0398 Consumption Module: Food and Drinks [2010-2013] hlf0371,hlf0372,hlf0373, hlf0374,hlf0375,hlf0376, hlf0377,hlf0378 Consumption Module: Furniture [2010-2013] hlf0427,hlf0428,hlf0429, hlf0430 Consumption Module: Health [2010-2013] hlf0387,hlf0388,hlf0389, hlf0390 Consumption Module: Hobby [2010-2013] hlf0403,hlf0404,hlf0405, hlf0406 Consumption Module: Holiday [2010-2013] hlf0407,hlf0408,hlf0409, hlf0410 [2023] Consumption Module: Insurance [2010-2013] hlf0411,hlf0412,hlf0413, hlf0414,hlf0415,hlf0416, hlf0417,hlf0418 Consumption Module: Internet [2010-2013] hlf0168,hlf0171 Consumption Module: Other [2010-2013] hlf0431,hlf0432,hlf0433, hlf0434 Consumption Module: Repair [2010-2013] hlf0419,hlf0420,hlf0421, hlf0422 Consumption Module: Telecommunication [2010-2013] hlf0391,hlf0392,hlf0393, hlf0394 Consumption Module: Transportation [2010-2013] hlf0423,hlf0424,hlf0425, hlf0426 Costs of comparable rental homes [1984-2002,2005- 2014] hlf0094 Costs of home ownership [1986-2014,2016- 2020] hlf0084,hlf0090_h, hlf0090_v1,hlf0090_v2, hlf0601,hlf0602,hlf0603, hlf0604,hlf0605 [2022, 2023] Dwelling / building type [1986-2020], [1986- 1990, 1991-2008, 2010-2018], [2009], [2016-2020] hlf0155_h,hlf0155_v1, hlf0155_v2,hlf0596 Financial burden of home ownership [2016] hlf0606 Financial burden of home rental [2016] hlf0611 continues on next page 46 Chapter 2. Topics of SOEP-Core SOEP Survey Paper 1261SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Table 6 – continued from previous page Questionnaire Module Years Variables Preview Governmentsubsidized housing [1984-2020], [1998- 2015], [1986- 2002,2008-2020] hlf0011_h,hlf0011_v1, hlf0011_v2,hlf0011_v3, hlf0011_v4,hlf0073 [2022, 2023] Groceries: Organic hli0150 [2023] Hereditary lease interest [1984-2013,2015- 2020], [1986-2020], [1986-1987], [1988- 1992], [1993-2020] hlf0016,hlf0154_h, hlf0154_v1,hlf0154_v2, hlf0154_v3 [2022, 2023] Home fixtures and furnishings [1991], [2015-2020] hlf0023,hlf0024,hlf0025, hlf0026,hlf0027,hlf0028, hlf0029,hlf0030,hlf0031, hlf0032,hlf0033,hlf0034, hlf0035,hlf0036,hlf0037, hlf0529,hlf0530,hlf0531 [2022, 2023] Home ownership / rental [1984-2020], [2003- 2020] hlf0001_h,hlf0001_v1, hlf0001_v2,hlf0001_v3, hlf0006,hlf0015 [2022, 2023] Home ownership / rental, ownership acquisition [1984-2020], [1984- 1990,1999-2001], [1991-1998], [1991- 1997] hlf0007_h,hlf0007_v1, hlf0007_v2,hlf0007_v3 Home ownership / rental, ownership change hlf0703 [2023] Home ownership / rental, ownership transfer [1999-2020] hlf0007_v4,hlf0009 [2022, 2023] Homeowner [1990-2020], [1990- 2002,2005-2012], [2003-2004], [2013- 2020] hlf0013_h,hlf0013_v1, hlf0013_v2,hlf0013_v3 Loans, mortgages, building loan agreements [1985-2020], [1985- 1990, 1991-1998], [1999-2020], [1984- 2020], [1984-1990, 1991-2001], [2002- 2020] hlf0087_h,hlf0087_v1, hlf0087_v2,hlf0088_h, hlf0088_v1,hlf0088_v2 Material deprivation (unregelmaessig) [2001-2015], [2016- 2019] hlf0175,hlf0177,hlf0178_h, hlf0178_v1,hlf0178_v2, hlf0178_v3,hlf0178_v4, hlf0178_v5,hlf0179, hlf0180,hlf0181,hlf0183, hlf0185,hlf0186,hlf0187, hlf0188,hlf0189,hlf0190, hlf0191,hlf0192,hlf0193, hlf0194,hlf0195,hlf0444, hlf0613,hlf0622 [2023] Modernization costs [2016-2020] hlf0599 Monthly rent, heating, other expenses [2016-2020] hlf0607,hlf0608,hlf0610 [2022, 2023] continues on next page 2.6. Home, Amenities, and Contributions of Private HH 47 SOEP Survey Paper 1261SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Table 6 – continued from previous page Questionnaire Module Years Variables Preview Monthly rent, heating, other expenses (Deutschmark) [2002-2014,2016- 2020] hlf0081_v2 [2022, 2023] Monthly rent, heating, other expenses: electricity [2010-2014,2016- 2020] hlf0078,hlf0079 [2022, 2023] Monthly rent, heating, other expenses: heating [1986-2014,2016- 2020], [1986- 1990,1997-2001], [2002-2014,2016- 2020] hlf0069_h,hlf0069_v1, hlf0069_v5 [2022, 2023] Monthly rent, heating, other expenses: heating and hot water [1990,1996], [1991- 1995] hlf0069_v2,hlf0069_v3, hlf0069_v4 Monthly rent, heating, other expenses: other [1991-2014,2016- 2020], [1991-2001], [1996-2014,2016- 2020] hlf0081_h,hlf0081_v1, hlf0082 Monthly rent, heating, other expenses: rent [1984-2020], [1984- 2001] hlf0074_h,hlf0074_v1 Monthly rent, heating, other expenses: rent (Deutschmark) [2002-2020] hlf0074_v2 [2022, 2023] Name and birth of children [1984-2020], [2017,2020] hlk0044_v1,hlk0044_v2 Number of books in household [2001,2006,2011,2016] hlf0197 Persons in household in need of care [1985-2020], [2015- 2020], [2016-2020] hlf0291,hlf0292,hlf0300, hlf0301,hlf0302,hlf0303, hlf0304,hlf0315_h, hlf0315_v1,hlf0315_v2, hlf0315_v3,hlf0317_h, hlf0317_v1,hlf0317_v2, hlf0317_v3,hlf0319, hlf0320,hlf0321,hlf0322, hlf0331,hlf0332,hlf0369, hlf0370_h,hlf0370_v1, hlf0370_v2,hlf0446, hlf0448,hlf0595,hlf0631 [2022, 2023] Pets [2006,2011,2016], [2006,2011] hlf0196,hlf0254,hlf0255, hlf0256,hlf0257,hlf0258, hlf0259 Photovoltaic and solar thermal system [2015-2016,2020] hlf0532,hlf0535,hlf0536, hlf0537,hlf0538,hlf0539 continues on next page 48 Chapter 2. Topics of SOEP-Core SOEP Survey Paper 1261SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Table 6 – continued from previous page Questionnaire Module Years Variables Preview Reasons for moving [1985- 2013,2015,2017-2020] hlf0108_h,hlf0108_v1, hlf0108_v10,hlf0108_v11, hlf0108_v12,hlf0108_v13, hlf0108_v14,hlf0108_v15, hlf0108_v2,hlf0108_v3, hlf0108_v4,hlf0108_v5, hlf0108_v6,hlf0108_v7, hlf0108_v8,hlf0108_v9, hlf0109,hlf0124,hlf0125 Reasons for moving, comparison of old and new home [2015,2017-2020], [2015,2017], [2015,2017,2019- 2020] hlf0524,hlf0525,hlf0526 [2022, 2023] Residential area (unregelmaessig) [1986-2019], [1994,1999,2004,2009,2014,2019], [2004,2009,2014,2016- 2020], [1994,1999,2004,2009,2014,2019], [1986-2020], [1986- 1987], [1988-1992], [1986,1994,1999,2004,2009], [2014, 2019], [1993- 2020] hlf0148,hlf0149,hlf0150, hlf0151,hlf0152,hlf0153_h, hlf0153_v1,hlf0153_v2, hlf0153_v3,hlj0004_v1, hlj0004_v2 [2022, 2023] Residential area, distances (unregelmaessig) [1986-2019] hlf0135,hlf0136,hlf0137, hlf0138,hlf0139,hlf0140, hlf0141,hlf0142,hlf0143, hlf0144,hlf0145,hlf0146, hlf0147 Residential area, neighbors (unregelmaessig) [1986-2019] hld0001,hld0002,hld0003 Savings hlf0722 [2023] Second Residence [2011,2016], [2011], [2011,2016] hlf0156,hlf0157,hlf0158 Size and condition of home, rooms [1984-2020], [1984- 1990,1998-2020] hlf0021_h,hlf0021_v1, hlf0021_v2,hlf0021_v3 [2022, 2023] Size and condition of home, size [1984-2020] hlf0018,hlf0019_h, hlf0019_v1,hlf0019_v2, hlf0019_v3,hlf0071_h, hlf0071_v1,hlf0071_v2, hlf0071_v3 [2022, 2023] Spendings, current hlf0428_v2, hlf0380, hlf0404_v4, hlf0424_v3, hlf0384_v3, hlf0408 [2023] Spendings, future hlf0714, hlf0715, hlf0716, hlf0717, hlf0718, hlf0719, hlf0720, hlf0721 [2023] Type of energy used in household ,hlf0591 [2023] continues on next page 2.6. Home, Amenities, and Contributions of Private HH 49 SOEP Survey Paper 1261SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Table 7 – continued from previous page Questionnaire Module Years Variables Preview Further education, course details and motives for participation, Course 3 Participation Certificate [1989,1993,2000,2004,2008]plg0186 Further education, course details and motives for participation, Course 3 Qualification for Promotion [1989,1993,2000,2004,2008]plg0143 Further education, course details and motives for participation, Course 3 Start [1989,1993,2000,2004,2008], [1989,1993], [2000,2004,2008], [1989,1993,2000,2004,2008] plg0110_h,plg0110_v1, plg0110_v2,plg0113 Further education, course details and motives for participation, Course 3 Telecourse [1989,1993,2000,2004,2008]plg0134 Further education, course details and motives for participation, Course 3 pay off [2004,2008] plg0116 Further education, course details and motives for participation, Course Subject / Content [1989,1993] plg0153 Further education, course details and motives for participation, Financial Support [1989,1993] plg0167,plg0168 Further education, course details and motives for participation, Initiative for taking Course [1989,1993] plg0166 Further education, course details and motives for participation, Pay Off [1989] plg0187,plg0188,plg0189 Further training measures, Further professional training [2014-2015], [2016- 2020] plg0269_v1,plg0269_v2 [2022] Further training measures, Trainig measures prev year [2014-2020] plg0270,plg0271 [2022] Further training, financing [2015-2018,2020] plg0285,plg0286,plg0287, plg0288,plg0289,plg0290, plg0291 continues on next page 56 Chapter 2. Topics of SOEP-Core SOEP Survey Paper 1261SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Table 7 – continued from previous page Questionnaire Module Years Variables Preview Further training, motivation plg0373i01, plg0374i01, plg0375i01, plg0376i01, plg0377i01, plg0373i02, plg0374i02, plg0375i02, plg0376i02, plg0377i02, plg0373i03, plg0374i03, plg0375i03, plg0376i03, plg0377i03 [2022] Further training, online plg0372i01, plg0372i02, plg0372i03 [2022] Further training, reasons for not taking part [2014] plg0277,plg0278,plg0279, plg0280,plg0281 Further training, results plg0378i01, plg0378i02, plg0378i03, plg0379i01, plg0379i02, plg0379i03, plg0380i01, plg0380i02, plg0380i03, plg0381i01, plg0381i02, plg0381i03 [2022] Further training, suggested / provided by employer [2014] plg0274 Further training, suggested / provided by employere [2014] plg0273 Lifelong learning [2014] plg0266 Vocational training, Currently in education / training [1984-2020], [2020] plg0012_v1,plg0012_v2 [2022, 2023] Vocational training, General school [1984-2015], [2016- 2020] plg0013_v1,plg0013_v3 Vocational training, Scholarship [2007-2020] plg0015_h,plg0015_v1, plg0015_v2,plg0015_v3, plg0015_v4 Vocational training, University [1984-1995], [1999- 2008], [2009-2012], [2013-2020] plg0014_v1,plg0014_v2, plg0014_v3,plg0014_v4, plg0014_v5,plg0014_v6, plg0014_v7 [2022, 2023] Youth Questionnaire Education and career plans [2013-2019], [2000- 2017], [2013-2019], [2000-2017] j_isco08_jobwish, j_isco88_jobwish, j_kldb2010_jobwish, j_kldb92_jobwish Education and career plans, Apprenticeship [2000-2020] jl0177,jl0182,jl0183,jl0203 [2022] Education and career plans, Career Training [2014-2020] jl0438,jl0439 [2022] Education and career plans, Engineering school [2013-2020] jl0440,jl0441 [2022] Education and career plans, Exploring Skills [2001-2020] jl0205 [2022, 2023] continues on next page 2.7. Education and Qualification 57 SOEP Survey Paper 1261SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Table 7 – continued from previous page Questionnaire Module Years Variables Preview Education and career plans, Financial Independence [2000-2020] jl0197,jl0198 [2022] Education and career plans, Informed about Future Occupation [2001-2020] jl0201 [2022] Education and career plans, No Particular Plans [2001-2020] jl0204 [2022, 2023] Education and career plans, Occupational Foundation [2000-2020] jl0179 [2022] Education and career plans, Occupational Integration [2000-2020] jl0178,jl0180,jl0181 [2022] Education and career plans, Parents Suggestions [2001-2020] jl0202 [2022, 2023] Education and career plans, Preferred Occupation [2000-2020] jl0199 [2022] Education and career plans, Vocational School [2000-2020] jl0184,jl0185 [2022] Education and career plans, Volunteering [2000-2020] jl0186,jl0187 [2022] Educational aspirations, Apprenticeship [2014-2020] jl0504 [2022] Educational aspirations, Aspired school-leaving qualification [2000-2020], [2000], [2001-2020], [2000- 2020] jl0130_h,jl0130_v1, jl0130_v2,jl0131 [2022] Educational aspirations, Career Training [2000-2020] jl0193 [2022, 2023] Educational aspirations, Civil Servant Training [2000-2020] jl0192 [2022, 2023] Educational aspirations, Completed Apprenticeship [2000-2020] jl0189 [2022, 2023] Educational aspirations, Engineering school [2000-2020] jl0194 [2022, 2023] Educational aspirations, Future Apprenticeship [2003-2020], [2000- 2020] jl0188,jl0196 [2022, 2023] Educational aspirations, Trade and Technical School [2000-2020] jl0191 [2022, 2023] continues on next page 58 Chapter 2. Topics of SOEP-Core SOEP Survey Paper 1261SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Table 7 – continued from previous page Questionnaire Module Years Variables Preview Educational aspirations, University [2000-2020] jl0195 [2022, 2023] Educational aspirations, Vocational School [2000-2020] jl0190 [2022, 2023] School, attendance & homework jl0125_v4, jl0125_v5 [2023] School, attendance & homework, Class Representative [2000-2020] jl0139 [2022] School, attendance & homework, Course type [2000-2020] jl0162,jl0163 [2022, 2023] School, attendance & homework, Extracurricular activities [2000-2020] jl0141,jl0142,jl0143,jl0144, jl0145,jl0146 [2022] School, attendance & homework, First Foreign Language [2000-2020], [2000], [2001-2005], [2006- 2020] jl0132_h,jl0132_v1, jl0132_v2,jl0132_v3 [2022] School, attendance & homework, Foreign Language jl1972 [2023] School, attendance & homework, Grade / Year [2014-2020] jl0434 [2022, 2023] School, attendance & homework, Grades / Points [2000-2020] jl0152,jl0153,jl0154,jl0155, jl0156,jl0157 [2022, 2023] School, attendance & homework, Graduation jl1973 [2023] School, attendance & homework, Number classmates [2000-2020], [2000], [2001-2018] jl0176_h,jl0176_v1, jl0176_v2 School, attendance & homework, Private School [2001-2018] jl0138 School, attendance & homework, Satisfaction with grades [2000-2020] jl0147,jl0148,jl0149,jl0150 [2022, 2023] School, attendance & homework, School attendance abroad [2000-2020] jl0137_h,jl0137_v1, jl0137_v2,jl0435,jl0436 [2022] School, attendance & homework, School recommendation [2001-2018] jl0151 School, attendance & homework, Schoolleaving certificate [2000-2020], [2000- 2011], [2012-2020] jl0127_h,jl0127_v1, jl0127_v2 [2022] continues on next page 2.7. Education and Qualification 59 SOEP Survey Paper 1261SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Table 7 – continued from previous page Questionnaire Module Years Variables Preview School, attendance & homework, Second Foreign Language [2000-2020], [2000], [2001-2005], [2006- 2020] jl0133_h,jl0133_v1, jl0133_v2,jl0133_v3 [2022] School, attendance & homework, Still in School [2000-2020], [2000- 2002], [2003-2005], [2006-2020] jl0125_h,jl0125_v1, jl0125_v2,jl0125_v3 [2022] School, attendance & homework, Student Body President [2000-2020] jl0140 [2022] School, attendance & homework, Year of leaving school [2000-2020] jl0126 [2022] School, attendance & homework, Year repeated [2000-2020] jl0164,jl0165,jl0166 [2022] Household Questionnaire Online learning materials [2021] hdigis1a_1,hdigis2a_1, hdigis3a_1,hdigis1b_2, hdigis2b_2,hdigis3b_2, hdigis1c_3,hdigis2c_3, hdigis3c_3,hdigis1d_4, hdigis2d_4,hdigis3d_4, hdigis1e_5,hdigis2e_5, hdigis3e_5,hdigis1f_6, hdigis2f_6,hdigis3f_6, hdigis1g_7,hdigis2g_7, hdigis3g_7,hdigis1h_8, hdigis2h_8,hdigis3h_8, hdigis1i_9,hdigis2i_9, hdigis3i_9,hdigis1j_10, hdigis2j_10,hdigis3j_10 Mother and Child Instruments Educational aspirations, Ideal school completion [2003-2020] idegrad1,idegrad2,idegrad3 Educational aspirations, intermediate secondary [2003-2020] probgra2 Educational aspirations, lower secondary [2003-2020] probgra1 Educational aspirations, upper secondary [2003-2020] probgra3 School and homework [2003-2020] scolcon1,scolcon2,scolcon3, scolcon4,scolcon5,scolcon6, scolcon7 School and homework, Comprehensive school [2003-2020] curscol7 School and homework, Grammar secondary class [2003-2020] curscol6 continues on next page 60 Chapter 2. Topics of SOEP-Core SOEP Survey Paper 1261SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Table 7 – continued from previous page Questionnaire Module Years Variables Preview School and homework, Intermediae secondary schol [2003-2020] curscol5 School and homework, Last report mark [2003-2020] lamark,matmark,nomark School and homework, Other schoool [2003-2020] curscol8 School and homework, Place [2003-2020] hwplace School and homework, Primary school [2003-2020] curscol1 School and homework, Second general school [2003-2020] curscol4 School and homework, Special pedagogic concept [2003-2020] curscol2 School and homework, Special school [2003-2020] curscol3 School and homework, Support [2003-2020] hwsupprt School enrollment [2003-2020] sclenrolm,sclenroln,sclenroly 2.8 Attitudes, Values, and Personality The attitudes, values, and personality modules provide extensive information on respondents’ personality traits, political orientations, concerns, satisfaction with different aspects of life, willingness to take risks, and much more. Questionnaire Module Years Variables Preview Individual Questionnaire 10,000-euro question [2010,2017] plh0134,plh0135,plh0136 Affective well-being [2007-2020] plh0184,plh0185,plh0186, plh0187 [2022, 2023] Anomie (irregular) [1990- 2018] plh0188,plh0189,plh0190, plh0191 [2023] continues on next page 2.8. Attitudes, Values, and Personality 61 SOEP Survey Paper 1261SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Table 8 – continued from previous page Questionnaire Module Years Variables Preview Attitudes towards genders [2019] plh0395i01,plh0395i02, plh0395i03,plh0395i04, plh0395i05,plh0395i06 Attitudes towards refugees [2016,2018,2020] plj0433,plj0434,plj0435, plj0436,plj0437,plj0438, plj0439,plj0440,plj0441, plj0442,plj0443 [2023] Big Five personality traits [2005,2009,2012- 2013,2017,2019], [2009,2012- 2013,2017,2019] plh0212,plh0213,plh0214, plh0215,plh0216,plh0217, plh0218,plh0219,plh0220, plh0221,plh0222,plh0223, plh0224,plh0225,plh0226, plh0255 [2023] Bundestag election, Eligibles [2014,2018] plh0333 [2022] Bundestag election, Non-eligibles [2021] ppolpar1,ppolpar2,ppolpar3 Depressive traits [2016,2019] plh0339,plh0340,plh0341, plh0342 [2023] Discrimination [2019] plh0387i01,plh0387i02, plh0387i03,plh0387i04, plh0387i05,plh0387i06, plh0387i07,plh0387i08, plh0387i09,plh0387i10, plh0387i11 Donation of blood [2010] plh0131_v1,plh0131_v2, plh0132,plh0133 Donations [2010,2015,2018,2020]plh0129,plh0130 Donations of goods [2010,2020] plj0108,plj0109,plj0110, plj0111,plj0112,plj0113, plj0114,plj0115 Flourishing [2015-2020] plh0334 [2022] Goals in life (Kluckhohn) (irregular) [1990-2016], [2013,2017- 2019], [2016] plh0104,plh0105,plh0106, plh0107,plh0108,plh0109, plh0110,plh0111,plh0112, plh0343_v1,plh0343_v2 Impulsivity, patience [2008,2013,2018] plh0253,plh0254 [2023] Income justice, general [2005] plh0116,plh0117,plh0118, plh0119,plh0120,plh0121, plh0122,plh0123,plh0124, plh0125,plh0126,plh0127 Inflation expectations pli0196, pinfgrp, pli0197, pli0198, pli0199, pli0200, pimmogrp, pli0201i01, pli0201i02, pli0201i03 [2023] Life satisfaction [1984-2020] plh0182 [2022, 2023] continues on next page 62 Chapter 2. Topics of SOEP-Core SOEP Survey Paper 1261SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Table 8 – continued from previous page Questionnaire Module Years Variables Preview Locus of control [1994-1996] plh0369,plh0370,plh0371, plh0372,plh0373,plh0374, plh0375,plh0376,plh0377_v1, plh0378_v1,plh0379_v1, plh0380_v1,plh0381_v1, plh0382_v1,plh0383_v1, plh0384_v1,plh0385_v1, plh0386_v1 Locus of control, rephrased [2005,2010,2015- 2016,2020] plh0377_v2,plh0378_v2, plh0379_v2,plh0380_v2, plh0381_v2,plh0382_v2, plh0383_v2,plh0384_v2, plh0385_v2,plh0386_v2 Loneliness [2013,2016- 2019], [2013,2016- 2020] plj0587,plj0588,plj0589 Lottery question [2004,2009,2014] plh0203 Money and account balance [2016,2018] plh0344,plh0345,plh0346 Optimism/pessimism [1999,2005,2009,2014,2019]plh0244 Organisational and community membership (irregular) [1985- 2019], (irregular) [2001-2019], [2003,2007,2011] plh0263_h,plh0263_v2, plh0264_h,plh0264_v1, plh0264_v2,plh0265,plh0266, plh0267 [2023] Organizational and community membership [1985,1989,1993] plh0263_v1 Policy objectives (Inglehart Index) [1984- 1986,1996,2006,2016] plh0054,plh0056,plh0058, plh0061 Political Tendency, Left- Right [2005,2009,2014,2019]plh0004 Political influence [2019] plh0397i01,plh0397i02, plh0397i03,plh0397i04, plh0397i05 Political orientation [1985-2020] plh0007 [2022, 2023] Political orientation (Party Affiliation) [1984-2020], [1984-1989], [1990], [1991], [1992], [1993], [1987-1988], [1994-2020] plh0012_h,plh0012_v1, plh0012_v2,plh0012_v3, plh0012_v4,plh0012_v5, plh0012_v6,plh0013_v1 [2022, 2023] Political orientation (Party Preference) [1984-2020] plh0011_h,plh0011_v1, plh0011_v2,plh0013_h, plh0013_v2 [2022, 2023] Reciprocity [2005,2010,2015- 2020] plh0206i01,plh0206i02, plh0206i03,plh0206i04, plh0206i05,plh0206i06 continues on next page 2.8. Attitudes, Values, and Personality 63 SOEP Survey Paper 1261SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Table 8 – continued from previous page Questionnaire Module Years Variables Preview Religious affiliation (irregular) [1990-2020], [2013,2016- 2020] plh0258_h,plh0258_v1, plh0258_v10,plh0258_v11, plh0258_v12,plh0258_v13, plh0258_v2,plh0258_v3, plh0258_v4,plh0258_v5, plh0258_v6,plh0258_v7, plh0258_v8,plh0258_v9 [2023] Religiousness plm0560 [2023] Risk aversion in different domains [2004,2009,2014] plh0197,plh0198,plh0199, plh0200,plh0201,plh0202 Risk aversion in general [2004,2006,2008- 2020], [2013], [2004,2006,2008- 2020] plh0204_h,plh0204_v1, plh0204_v2 [2022, 2023] Satisfaction with various aspects (irregular) [1989-2019], [2006,2011- 2013,2016], [1984-2020], [2008-2020], [1984-2020], [1984- 1990,1993- 2020], [1984- 2020], [2004- 2020], [1984- 2020], [1984- 1989,1991- 1994,1996- 2020], [1990,1997- 2020], [2006- 2020] plh0164,plh0171,plh0172, plh0173,plh0174,plh0175, plh0176,plh0177,plh0178, plh0179,plh0180,plh0181 [2022, 2023] Self-esteem [2010,2015- 2020] plh0206i11 Social justice [2019] plh0396i01,plh0396i02, plh0396i03,plh0396i04 Social responsibility [1997,2002,2017] plh0016,plh0017,plh0018, plh0019,plh0020,plh0021, plh0022,plh0023,plh0024, plh0025,plh0026 Tendency to forgive [2010,2015- 2016,2020] plh0206i07,plh0206i08, plh0206i09,plh0206i10 Trust, trustworthiness and fairness [2003,2008,2013,2018]pld0043,pld0044,pld0045, plh0192,plh0193,plh0194, plh0195,plh0196 [2023] Wage justice [2015], [2017,2019], [2017-2019] plh0138,plh0139,plh0140, plh0141,plh0337_v1, plh0337_v2,plh0338_v1, plh0338_v2 [2023] continues on next page 64 Chapter 2. Topics of SOEP-Core SOEP Survey Paper 1261SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Table 8 – continued from previous page Questionnaire Module Years Variables Preview Well-being aspects [1994,1998- 1999] plh0091_v2,plh0092_v2, plh0093_v2,plh0094_v2, plh0095_v2,plh0096_v2, plh0097_v2,plh0098_v2, plh0099_v2,plh0100_v2, plh0101,plh0102,plh0103 Well-being aspects, East Germany [1990-1991] plh0091_v1,plh0092_v1, plh0093_v1,plh0094_v1, plh0095_v1,plh0096_v1, plh0097_v1,plh0098_v1, plh0099_v1,plh0100_v1 Worries [2009- 2014,2019] plh0032,plh0033,plh0034, plh0035,plh0038,plh0040, plh0042,plh0043,plh0046, plh0047,plh0335,plh0336 [2022, 2023] Youth Questionnaire Affective well-being [2007-2020] jl0381,jl0382,jl0383,jl0384 [2022, 2023] Attitudes and opinions [2000-2020] jl0329,jl0330,jl0360,jl0364 [2022] Big Five personality traits [2006-2020] jl0365,jl0366,jl0367,jl0368, jl0369,jl0370,jl0371,jl0372, jl0373,jl0374,jl0375,jl0376, jl0377,jl0378,jl0379,jl0380 [2022, 2023] Future [2000-2020] jl0222,jl0223,jl0224,jl0225, jl0226,jl0227,jl0228,jl0229, jl0230,jl0231,jl0232 Life satisfaction [2006-2020] jl0392 [2022, 2023] Locus of control [2006-2020] jl0350_v1,jl0351_v1, jl0352_v1,jl0353_v1, jl0354_v1,jl0355_v1, jl0356_v1,jl0357_v1, jl0358_v1,jl0359_v1 Locus of control, rephrased [2001-2005] jl0350_v2,jl0351_v2, jl0352_v2,jl0353_v2, jl0354_v2,jl0355_v2, jl0356_v2,jl0357_v2, jl0358_v2,jl0359_v2 [2022] Political orientation [2006-2020] jl0388,jl0389,jl0390,jl0391 [2022] Risk aversion in general [2006-2020] jl0349 [2022, 2023] Social justice [2019-2020] jl1909,jl1910,jl1911,jl1912 [2022] Sources of social inequality [2000-2020] jl0337,jl0338,jl0339,jl0340, jl0341,jl0342,jl0343,jl0344, jl0345,jl0346,jl0347,jl0348 [2022] Trust [2006-2020] jl0361,jl0362,jl0363 [2022, 2023] Mother and Child Instruments Big Five personality traits [2003-2020] char10,char1a,char1b,char2, char3,char4,char5,char6, char7,char8,char9 continues on next page 2.8. Attitudes, Values, and Personality 65 SOEP Survey Paper 1261SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Questionnaire Module Years Variables Preview Individual Questionnaire Applying for German citizenship (unregelmaessig) [1998-2018] plj0021 Circle of friends, percentage of migrants [2013,2018] plm0143 Contacts abroad, thoughts about moving abroad [2009,2014,2019] plj0089,plj0090,plj0091, plj0092,plj0104,plj0105 Country of origin [2020] plj0725 [2022, 2023] Disadvantage / discrimination based on ethnic origins (detailed) [2019] plh0387i01,plh0387i02, plh0387i04,plh0387i05, plh0387i06,plh0387i07, plh0387i08,plh0387i09, plh0387i10,plh0387i11, plj0048_v1,plj0048_v2, plj0327,plj0328,plj0329, plj0330,plj0331,plj0332, plj0333,plj0334,plj0335, plj0336,plj0337,plj0338, plj0339 Discrimination, areas plh0412, plh0414, plh0414 [2022] Foreign language skills [2013] plm0135 Integration indicators (unregelmaessig) [1984-2018], (unregelmaessig) [1997-2019], [2020] plj0078,plj0080_v1, plj0080_v2 [2022] Intention to stay [1996-2011,2015- 2020], [2013], [1996- 2011,2013,2015- 2020], [2020], [1984- 2011,2013,2015- 2020], [1996- 2011,2013,2015-2020] plj0085_v1,plj0085_v2, plj0086_v1,plj0086_v2, plj0087,plj0088 Language ability German [2007-2011,2013- 2020], [2010- 2011,2013-2020] plj0071,plj0072,plj0073 [2023] Language ability native language [2007-2011,2013- 2019], [2010- 2011,2013-2019] plj0074,plj0075,plj0076 [2023] Language use, media [2014,2016], [2017- 2020] plj0226_v1,plj0226_v2 [2022] Language use, newspapers (unregelmaessig) [1988-2012] plj0070 Native language [2007- 2011,2013,2015-2019] plj0009 Native language (family) [2013,2015-2020], [2013], [2015-2020] plm0136_h,plm0136_v1, plm0136_v2 Native language (friends) [2013,2015-2020], [2013], [2015-2020] plm0137_h,plm0137_v1, plm0137_v2 Native language (workplace) [2013,2015-2020], [2013], [2015-2020] plm0138_h,plm0138_v1, plm0138_v2 continues on next page 72 Chapter 2. Topics of SOEP-Core SOEP Survey Paper 1261SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Table 10 – continued from previous page Questionnaire Module Years Variables Preview Regional attachment [2009,2014,2019] plj0043,plj0044,plj0045 Sense of home (unregelmaessig) [1988-2012], [2014] plj0083,plj0340 Translation help [2013-2018] p_buh1,p_buh10,p_buh2, p_buh3,p_buh4,p_buh5, p_buh6,p_buh7,p_buh8, p_buh9 Visited country of origin in last 2 years [2014,2016,2018,2020] plj0322,plj0323 [2022] Visiting / being visited by Germans and foreigners at home (unregelmaessig) [2007-2019] plj0060,plj0061,plj0062, plj0063 Youth Questionnaire Language ability German [2006-2018], [2010- 2018], [2014-2020] jl0248,jl0442,jl0443,jl0444, jl1249 [2023] Language ability native language [2006-2013], [2010- 2013] jl0251,jl1251 Biography Questionnaire German language courses, Federal Office for Migration and Refugees plm0721_v2 [2023] German language courses, before moving to Germany lm0131_v5 [2023] German language courses, since moving to Germany lr3579 [2023] German language exam, before moving to Germany lm1064i01_v4 [2023] German language exam, since moving to Germany lb1450 [2023] Moving to Germany, COVID-19 lb1442 [2022] Moving to Germany, Obstacles lr3577i01, lr3577i02, lr3577i03, lr3577i04, lr3577i05, lr3577i06, lr3577i07, lr3577i08, lr3577i09, lr3577i10, lr3577i11 [2022] 2.10. Integration, Migration, Transnationalization 73 SOEP Survey Paper 1261SOEP Survey Paper 1261 SOEPcompanion, Release 2023 2.11 Survey Methodology Survey methodology modules offer diverse variables on imputation, weighting, SOEP-Core fieldwork, identifiers, interview methods, survey modes, and information about the respondent’s exit from the survey. Questionnaire Module Variables Interviewer Questionnaire Identificators hhnr ,intid ,syear ,wave Interview information typint ,lenghtinth ,lenghtintp ,lenghtintj Demography gender ,birth ,marital ,educ ,modbula , modggk ,ista1 ,ibstam1 ,ibstav1 ,imusp ,irel Interviewer history startint ,endint ,experience ,firstintm , firstintd ,lastintm ,lastintd Employment iberuf ,ioed ,istell Interviewer activity meancontacthh ,responserate ,amountinth , amountintp ,amountintj ,papi ,capi ,cawi , mail Patience iged Health iges Risk aversion irisk Life satisfaction izule Incentives ibbarhon ,ibbeval Optimism ibopt Motivation & Fulfillment igru01 -igru07 ,ierf01 -ierf14 Assessment of Participation itebe01 -itebe13 Interviewer Training ibseval01 -ibseval04 ,ibschul ,ibschul02 Big Five personality traits iego01 -iego22 Attitudes and social interaction ibez01 -ibez05 ,iverh01 -iverh06 Political orientation ipol1 -ipol4 Worries isor01 -isor14 ,isor21 -isor22 Working hours ibwsist01 -ibwsist05 ,ibwssol01 -ibwssol03 Interviewer and other studies ibsozer01 -ibsozer08 ,ibsozerno ,ibsozerso , ibef01 -ibef03 Foreign language skills ispr01 -ispr10 ,ibspre01 -ibspre10 Flags (conflicts) genderconfl ,birthconfl ,maritalconfl ,educconfl ,startintconfl ,ista1confl Important documents regarding this Topic are available here Last change: Jul 24, 2023 74 Chapter 2. Topics of SOEP-Core SOEP Survey Paper 1261SOEP Survey Paper 1261 CHAPTER THREE SURVEY DESIGN 3.1 SOEP Questionnaires The interview methodology of the SOEP is based on a set of pre-tested questionnaires for households and individuals. Interviewers try to obtain face-to-face interviews with all members of a given survey household aged 16 and over. Thus, there are no proxy interviews for adult household members. Additionally, one person (the “head of household”) is asked to answer a household-related questionnaire covering information on housing, housing costs, and different sources of income (e.g., social transfers such as social assistance or housing allowances). This questionnaire also includes questions on children up to the age of 16 in the household, mainly concerning daycare, kindergarten, and school attendance. The questions in the SOEP are largely identical for all participants of the survey to ensure comparability across the participants within a given year, but of course there are differences across years. There are a few exceptions to this rule, which are due to different requirements in the target population. Up to 1996, the questionnaires for the sample of foreigners (B) and the immigrant sample (D) covered additional measures of integration or information on re-migration behavior. Between 1990 and 1992, i.e., during the first years of the German reunification process, the questionnaire for the East German sample (C) also contained some additional specific variables. From 1996 to 2012, all questionnaires were uniform and completely integrated for all of the main SOEP samples. For the IAB-SOEP Migration Sample, which was launched in 2013, specific questions were added to the SOEP questionnaires. The same is true of the IABBAMF-SOEP Survey of Refugees, which was launched in 2016. Another special questionnaire is used for first-time respondents since some questions do not have to be repeated every year. Each respondent is asked to fill out a biographical questionnaire covering information on the life course up to the first SOEP interview (e.g., marital history, social background, and employment biography). Additional information not provided directly by the respondent can be obtained from the “address logs”, which are stored for every year in the $PBRUTTO and $HBRUTTO files. Every address log is filled in by the interviewer even in the case of non-response, thus providing very valuable information, e.g. for attrition analysis. For researchers interested in methodological issues, these data also contain information on the fieldwork process such as the number of contacts, reasons for drop-outs, and interview mode. For households that were contacted successfully, the address logs cover the size of the household, some regional information, survey status, etc. The individual data for all household members include the relationship to the household head, survey status of the individual, and some demographic information. Life History 75 SOEP Survey Paper 1261SOEP Survey Paper 1261 SOEPcompanion, Release 2023 The SOEP questionnaires are designed so that people in a SOEP household can be analyzed from birth to adulthood and throughout the rest of their lives. In addition to the Youth Questionnaire, which was conducted for the first time in 2000/01, a series of questionnaires for specific cohorts of children living in SOEP households have been introduced since 2003. These have been completed annually since their year of introduction by mothers (in exceptional cases by fathers) with children of the appropriate age. In 2003, a questionnaire was developed for the mothers of newborn children, Mother and Child Questionnaire (Newborns). The following instruments were developed in such a way that this starting cohort (born 2002/2003) can be followed up in their development and analyzed longitudinally. This was followed in 2005 by a questionnaire for mothers of 2-3-year-old children, Mother and Child Questionnaire (2-3-year- olds) and in 2008 by a questionnaire for 5-6-year-olds, Mother and Child Questionnaire (5-6-year-olds). In 2010, the questionnaire for 7-8-year-old children, Parents and Child Questionnaire (7-8-year-olds), completed by both mothers and fathers, was launched. In 2012, the questionnaire for 9-10-year-old children, Mother and Child Questionnaire (9-10-year-olds) was added as the last questionnaire to be answered by the mothers. This was followed by two youth instruments in which the children, aged 12, Pre-Teen Questionnaire and 14, Early Youth Questionnaire, answered questions about their own lives for the first time. These were introduced in 2014 and 2016, respectively. In 2018, the first cohort completed the entire battery of age-specific instruments and from then on, they will complete the annual questionnaires of the long-term SOEP study. Each person in a SOEP household receives the Individual Questionnaire as soon as they reach the age of 18, and the head of the household also receives the Household Questionnaire. If a respondent states in their interview that someone has died in the last year, regardless of whether the deceased person was part of a SOEP household, the Deceased Individual Questionnaire is given to the respondent providing the information. 76 Chapter 3. Survey Design SOEP Survey Paper 1261SOEP Survey Paper 1261 SOEPcompanion, Release 2023 3.1.1 Overview of the Questionnaires 3.1.2 Household Questionnaire The household questionnaire in its basic form has been an important part of the SOEP surveys since 1984 and has been improved and expanded continuously. The data collected and the questionnaire itself have become so complex that the original topics are no longer sufficient. Between 1984 and 2016, the number of questions more than doubled from 46 to 97. The multitude of questions offer users many options for analysis. Each year, the number of questions varies because new innovative question modules are added or because some questions are not asked every year. An overview of the modules included at different intervals can be found in the section Topics of SOEP-Core. The questions provide diverse information about the respondents’ households that is stored in several hundred variables. Child-specific questions asked in the household questionnaire are found in the separate dataset $kind. Availability: Since 1984 Dataset: $h (CS), hl (long) Respondent: Head of household The following question modules are part of the core program of the Household Questionnaire: •Change of living situation •Neighborhood •Building type •Size and condition of dwelling •Amenities •Type of dwelling •Loans, mortgages, building-society loans •Hereditary lease interest •Modernization costs •Ownership costs 3.1. SOEP Questionnaires 77 SOEP Survey Paper 1261SOEP Survey Paper 1261 SOEPcompanion, Release 2023 •Photovoltaic and solar thermal system •Owner debt •Government-subsidized housing •Home ownership •Rental and expenses •Tenant debt •Cleaning or household assistance •Persons in need of care •Names and birth dates of children •Child’s school attendance •Childcare situation •Income and expenses from renting/leasing •Loan repayment •Debt •Inheritances, gifts, winnings •Investments •Income/expenses household •Savings •Material deprevation •Number of books •Pets •Cause of moving where applicable: +migration-specific modules for the IAB-SOEP Migration Sample •distinguishing repayment of loans, debt, income / expenses between Germany and foreign country or where applicable: +refugee-specific modules for the IAB-BAMF-SOEP Sample of Refugees •Information on shared accomodations •Location preferences 78 Chapter 3. Survey Design SOEP Survey Paper 1261SOEP Survey Paper 1261 SOEPcompanion, Release 2023 3.1.3 Individual Questionnaire The individual questionnaire has been a standard instrument since the beginning of the SOEP. In order to enable analysis over time, the individual questionnaire has a large number of question modules that are asked every year. There are also questions that do not have to be asked every year, as short-term changes are unlikely. In order to be able to react to current social changes, new topics are added to the individual questionnaire and repeated at intervals of more than one year. Availability: Since 1984 Dataset: $p (CS), pl (long) Respondent: Persons over 18 years in the household The following question modules are part of the core program of the Individual Questionnaire: •Satisfaction with various live aspects •Satisfaction with current life situation •Feelings •Flourishing •Risk aversion •Political orientation •Worrying •Life satisfaction overall •Ethnic/national origins •Vocational training •Completed level of education •Higher education •Family situation •Family changes •State of health •Disability or severe disability •Visits to the doctor •Hospital stays •Sick leave •Health insurance •Wages and collective wage agreements •Additional questions for employees •Additional questions for retirees/pensioners •Government transfers •Calendar •Time use •Second jobs 3.1. SOEP Questionnaires 79 SOEP Survey Paper 1261SOEP Survey Paper 1261 SOEPcompanion, Release 2023 •Income •Work, last 7 days •Maternity/ parental leave •Care period (Pflegezeit) •Registered unemployed •Quitting a job •Employment status •Start of job •Change of job •Job search •Current profession •Current job •Working hours •Overtime •Optimism •Religion •Organization and Association membership •Personality traits (Big Five) •Anomie •Life goals •Locus of control •Reciprocity •Trust and Fairness •Narcissism •Lonelisness •Impulsiveness and Patience •Political Goals (Ingelhart-Index) •Attitude towards refugees •Just society •Discriminatiom •Bundestag election •Social responsibility •Influence on public decisions •Friends •LGBT-Status •Child wish 80 Chapter 3. Survey Design SOEP Survey Paper 1261SOEP Survey Paper 1261 SOEPcompanion, Release 2023 •Gender stereotypes •Attitudes towards gender •On-Call occupation •Commuting •Home-Office •Short-Time work payment •Work council •Payment equity •Workload •Occupational expectations •Depressive traits •Smoking and drinking •Integration indicators •Free time •Leisure activities •Donation where applicable: +migration specific modules for the IAB-SOEP-Migrationsample •First Job in Germany •Job before immigration •Language proficiency before and since immigration •Partnership during immigration •Living situation since immigration •Religion and faith of parents •Satisfaction in various areas of life before and after immigration or where applicable: +refugee specific modules for the IAB-BAMF-SOEP-Sample of Refugees •Legal status •Religion and faith •Language proficiency •Integration courses and government measures •Special questions for interviewers concerning language •Recognition of qualifications Re-Interviewed •Cultural and political participation •Application for recognition 3.1. SOEP Questionnaires 81 SOEP Survey Paper 1261SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Cognitive Tests for Youth In 2006, a separate questionnaire with cognitive tests for adolescents was used for the first time in the SOEP. It was named “Lust auf DJ” (or “interest in DJ”) as a play on disc jockey, but DJ stands for “Denksport und Jugend”, or mind sports and youth. The questionnaire was created for young people between the ages of 16 and 17. Availability: Since 2007 Dataset: cogdj (CS) Respondent: 16-17-year-olds in the household as a supplement to the youth questionnaire Content: •Assignment of word pairs •Complete equations •Assign figures 3.1.7 Additional Instruments Catch-Up Individual Questionnaire The Catch-Up or “Gap” (German:Lücke) questionnaire is given to respondents who failed to respond in the previous year of the study. They are asked to provide important data about the year they missed. Availability: Since 1987 Dataset: pluecke (CS), plueckel (long) Respondent: SOEP respondents who are temporarily unavailable. Content: All data refer to the previous survey year •Status of the respondent •Occupational change •Receipt of social benefits within the last year •Completion of education •Type of educational attainment •Change of family status Deceased Individual Questionnaire In 2009, for the first time in SOEP-Core, information was collected on former SOEP participants who had died since the last survey in 2008. The Deceased Individual questionnaire thus completes the life history information in the SOEP. The primary aim is to obtain as much information as possible about the causes and circumstances of death of former SOEP respondents. As the questionnaire also collects information on individuals who have never participated in the SOEP survey, this can be used together with the causes and circumstances of death in socio-scientific analysis. Availability: Since 2009 Dataset: vp (CS), vpl (long) Respondent: SOEP respondents who lost a loved one. 88 Chapter 3. Survey Design SOEP Survey Paper 1261SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Content: •Relationship to the deceased •Was the deceased a survey respondent? •Domestic environment of the deceased •Cause and place of death •Last will and testament •Health of the deceased •Life satisfaction of the deceased •Influence of bereavement on respondent’s own life Grip Strength Test Availability: Since 2008 Dataset: gripstr (long) Respondent: Persons over 17 years in the household Content: This test measures hand grip strength, which is useful in assessing respondents’ physical condition. Interviewer Questionnaire We derive basic demographical and employment information on interviewers from personnel data of the fieldwork organization. Since 2000, Kantar Public regularly updates these information. Additionally, at irregular intervals, the SOEP interviewers complete a short version of the standard individual questionnaire themselves, which is called the interviewer questionnaire. Availability: 2006, 2012, 2016 Dataset: interviewer (long) Respondent: SOEP interviewers Content: •Basic Demography •Occupational History •Personality •Motivation •Interviewer Training •Worries •Language Skills Last change: May 16, 2023 3.1. SOEP Questionnaires 89 SOEP Survey Paper 1261SOEP Survey Paper 1261 SOEPcompanion, Release 2023 3.2 Scales Manual Introduction This manual briefly describes the theoretical background and development of all of the scales used in the Socio- Economic Panel (SOEP) study. It also provides the relevant citations as well as the items belonging to the scales and the answer format, including the verbal anchors. The unique value of this manual lies in the presentation of each scale in the form of easy-to-understand tables listing variable names of the items in the scale in a wave-specific dataset (labels). This allows the individual items to be found and aggregated quickly. In addition, the tables in this manual include mean values (M), standard deviations (SD), corrected item-total correlations (CITC), and information as to whether the item has to be recoded before aggregation (R). The number of valid cases in the particular survey years, as well as two measures of reliability, the internal consistency (Cronbach’s Alpha) and test-retest correlations (if available), are also reported. 3.2.1 Affective Well-Being Summary Affective well-being describes the balance between positive and negative emotional experiences and is of interest for the research on well-being in psychology and economics as well as in the social sciences in general. The scale consists of four items and has been used in the SOEP since 2007. Theoretical Background In the psychological literature, subjective well-being is assumed to consist of two components: cognitive well-being and affective well-being (Schimmack et al., 2002). Here, affective well-being represents the emotional component of subjective well-being. In contrast to cognitive well-being, which is based on reflexive evaluation of subjective well-being, affective well-being hinges on the balance between positive and negative emotions (Sumner, 1996). The distinction between cognitive and affective well-being is important for the scientific investigation of subjective wellbeing and for policy considerations about public well-being. If the aim is to maximize subjective well-being on both the individual and the social level, and if the two are indeed separate components, it is crucial to measure their relative importance. Despite the importance of distinguishing between cognitive and affective well-being, their relationship has been researched little to date (Schimmack, 2009). Their relationship also highlights the need to examine factors such as unemployment that potentially influence subjective well-being (e.g., Schimmack et al., 2008). Conceptually, the relationship between affective well-being and other general personality characteristics is also of interest. Scale Development Theoretical considerations and results from the 2006 SOEP pretest led to the construction of a scale consisting of four items. One item deals with positive experiences (“happy”), while the other three items deal with negative experiences (annoyed, afraid, sad; Schimmack, 2009). In the pilot study, the measure constructed in this manner showed a high correlation with a longer measure of affective balance consisting of 10 items and produced results that are consistent with the previous results in the relevant psychological literature (Schimmack et al., 2008). References Schimmack, U. 2009. Measuring wellbeing in the SOEP. Schmollers Jahrbuch, 129, 241-249. Schimmack, U. Diener, E., & Oishi, S. (2002). Life-Satisfaction is a momentary judgment and a stable personality characteristic: The use of chronically accessible and stable sources. Journal of Personality, 70, 345-384. Schimmack, U., Schupp, J., & Wagner, G. G. (2008). The influence of environment and personality on the affective and cognitive component of subjective well-being. Social Indicators Research, 89, 41-60. Sumner, L. W. (1996): Welfare, happiness, and ethics. Oxford: University Press. 90 Chapter 3. Survey Design SOEP Survey Paper 1261SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Items I will now read to you a number of feelings. Please indicate for each feeling how often or rarely you experienced this feeling in the last four weeks (Ich lese Ihnen eine Reihe von Gefühlen vor. Geben Sie bitte jeweils an, wie häufig oder selten Sie dieses Gefühl in den letzten vier Wochen erlebt haben): 1. Angry (ärgerlich gefühlt)? 2. Worried (ängstlich gefühlt)? 3. Happy (glücklich gefühlt)? 4. Sad (traurig gefühlt)? Scale: 1 (Very rarely / Sehr selten) to 5 (Very often / Sehr oft) Test-Retest Correlations In 2009, this scale was included in a retest taken by a subsample (N = 164 completed the scale) within 30 to 49 days after the initial test. Test-retest correlations for the items were (in scale order) .46, .49, .51, and .46; scale scores correlated .54. Items and Scale Statistics year variable count mean sd itemrestcorr alpha 2007 plh0184 20829 2.89 1.03 0.40 0.66 2007 plh0185 20796 1.96 1.01 0.49 0.66 2007 plh0186R 20815 2.53 0.87 0.30 0.66 2007 plh0187 20819 2.39 1.04 0.57 0.66 2008 plh0184 19631 2.82 0.99 0.41 0.68 2008 plh0185 19602 1.94 0.98 0.50 0.68 2008 plh0186R 19605 2.52 0.86 0.36 0.68 2008 plh0187 19620 2.38 1.03 0.57 0.68 2009 plh0184 20722 2.78 1.00 0.39 0.65 2009 plh0185 20666 1.93 0.98 0.49 0.65 2009 plh0186R 20691 2.55 0.88 0.29 0.65 2009 plh0187 20689 2.37 1.02 0.56 0.65 2010 plh0184 18859 2.74 0.96 0.38 0.66 2010 plh0185 18844 1.95 0.96 0.49 0.66 2010 plh0186R 18850 2.52 0.86 0.33 0.66 2010 plh0187 18852 2.38 1.02 0.58 0.66 2011 plh0184 20969 2.75 1.01 0.40 0.65 2011 plh0185 20936 1.97 0.98 0.50 0.65 2011 plh0186R 20954 2.49 0.88 0.28 0.65 2011 plh0187 20965 2.41 1.02 0.57 0.65 2012 plh0184 20753 2.72 0.99 0.40 0.67 2012 plh0185 20741 1.91 0.96 0.50 0.67 2012 plh0186R 20739 2.45 0.85 0.35 0.67 2012 plh0187 20745 2.33 1.01 0.56 0.67 2013 plh0184 25919 2.81 1.00 0.42 0.68 2013 plh0185 25895 1.91 0.96 0.50 0.68 2013 plh0186R 25909 2.43 0.84 0.35 0.68 2013 plh0187 25921 2.35 1.01 0.58 0.68 2014 plh0184 27400 2.80 1.02 0.42 0.68 2014 plh0185 27379 1.90 0.97 0.50 0.68 2014 plh0186R 27384 2.39 0.84 0.35 0.68 2014 plh0187 27384 2.34 1.03 0.57 0.68 continues on next page 3.2. Scales Manual 91 SOEP Survey Paper 1261SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Table 1 – continued from previous page year variable count mean sd itemrestcorr alpha 2015 plh0184 25338 2.79 1.00 0.42 0.67 2015 plh0185 25307 1.92 0.97 0.49 0.67 2015 plh0186R 25332 2.40 0.84 0.36 0.67 2015 plh0187 25320 2.33 1.02 0.56 0.67 2016 plh0184 24474 2.77 1.02 0.42 0.67 2016 plh0185 24451 1.96 0.98 0.49 0.67 2016 plh0186R 24464 2.40 0.84 0.32 0.67 2016 plh0187 24459 2.32 1.01 0.56 0.67 2017 plh0184 26742 2.75 1.00 0.41 0.67 2017 plh0185 26734 1.92 0.97 0.51 0.67 2017 plh0186R 26738 2.36 0.82 0.34 0.67 2017 plh0187 26733 2.31 1.01 0.55 0.67 2018 plh0184 25850 2.75 1.01 0.42 0.67 2018 plh0185 25836 1.90 0.97 0.50 0.67 2018 plh0186R 25845 2.37 0.83 0.36 0.67 2018 plh0187 25847 2.30 1.01 0.56 0.67 2019 plh0184 26013 2.72 1.02 0.42 0.67 2019 plh0185 25992 1.87 0.96 0.51 0.67 2019 plh0186R 26004 2.35 0.82 0.34 0.67 2019 plh0187 26009 2.28 1.00 0.56 0.67 2020 plh0184 26064 2.72 1.01 0.42 0.67 2020 plh0185 26063 2.06 1.03 0.49 0.67 2020 plh0186R 26062 2.38 0.84 0.36 0.67 2020 plh0187 26061 2.32 1.01 0.56 0.67 3.2.2 Anomie Summary Anomie describes the individual’s subjective response to a community and social environment that is perceived to be threatening and unregulated. Anomie is expressed in an individual tendency towards loss of motivation and feelings of despair and helplessness (Srole, 1956). The four-item scale has been used in the SOEP at irregular intervals in the 90s and at regular five-year intervals since 2008. Theoretical Background Anomie refers to a condition of normlessness, that is, a lack of social norms. Durkheim (1897, 1951) introduced the concept of anomie in sociology to describe the erosion of social norms and societal rules under conditions of farreaching structural change—for example, the conditions that arise in times of rapid social and economic transformation. The result is a breakdown of bonds between the individual and the community or society. Merton (1938) applied and expanded the concept of anomie in his theory of deviant behavior. He extended Durkheim’s understanding by observing the factors that lead to anomie. In Merton’s view, anomie may occur when (1) cultural goals and desires are prescribed as normative for a society as a whole; (2) the legitimate means used to achieve these goals are strictly regulated; and (3) these legitimate means are unequally distributed in the society. Anomie is then the result of a state in which the individual adheres to the society’s main cultural ideas and principles but does not possess the legitimate means to attain them. The result may be various forms of deviant behavior such as criminal acts. Durkheim (1897, 1951) treated anomie primarily as a social condition, whereas Merton (1938) shifted the focus to the individual. Srole (1956) followed on Merton’s (1938) understanding, contributing the social psychological construct of anomia to the constellation of themes surrounding anomie. Anomia relates to the individual, psychological side of a social condition that is perceived to be anomic. In this regard, the concept of anomia shows certain similarities with external locus of control. Scale Development 92 Chapter 3. Survey Design SOEP Survey Paper 1261SOEP Survey Paper 1261 SOEPcompanion, Release 2023 The anomie scale taken from the German Welfare Survey (Duttenhöfer & Schröder, 1996) was shortened for the SOEP survey and one positive item was added. References Durkheim, E. (1951). Suicide, a study in sociology. Glencoe, Ill.: Free Press. Duttenhöfer, S. & Schröder, H. (1996). Die Wohlfahrtssurveys 1978-1993 - Variablenübersicht. Zuma-Technischer Bericht 94/11. Mannheim. Merton, R. K. (1938). Social structure and anomie. American Sociological Review, 3, 672-682. Srole, L. (1956). Social integration and certain corollaries: An exploratory study. American Sociological Review, 21, 709-716. Items To what extent do the following statements apply to you (Wie sehr stimmen die folgenden Aussagen für Sie persönlich): 1. When I think about the future, I’m actually quite optimistic. (Wenn ich an die Zukunft denke, bin ich eigentlich sehr zuversichtlich.) 2. I often feel lonely. (Ich fühle mich oft einsam.) 3. I don’t really enjoy my work. (Meine Arbeit macht mir eigentlich keine Freude.) 4. Things have gotten so complicated that I almost can’t manage anymore. (Die Verhältnisse sind so kompliziert geworden, dass ich mich fast nicht mehr zurecht finde.) Scale: 1 (Completely / Stimmt ganz und gar) to 4 (Not at all / Stimmt ganz und gar nicht) Test-Retest Correlations In 2005, this scale was included in a retest taken by a subsample (N = 126 completed the scale) within 30 to 49 days after the initial test. Test-retest correlations for the items were (in scale order) .39, .52, .30, and .52; scale scores correlated .60. Items and Scale Statistics year variable count mean sd itemrestcorr alpha 1990 plh0188R 4432 2.73 0.84 0.20 0.46 1990 plh0189 4413 3.36 0.97 0.28 0.46 1990 plh0190 3430 3.31 0.88 0.23 0.46 1990 plh0191 4421 2.99 0.94 0.35 0.46 1991 plh0188R 4179 2.62 0.86 0.27 0.48 1991 plh0189 4161 3.33 0.94 0.26 0.48 1991 plh0190 3765 3.30 0.89 0.23 0.48 1991 plh0191 4157 2.89 0.92 0.34 0.48 1992 plh0188R 11000 2.68 0.80 0.27 0.55 1992 plh0189 10991 3.28 0.93 0.36 0.55 1992 plh0190 10498 3.31 0.85 0.31 0.55 1992 plh0191 10989 3.23 0.89 0.41 0.55 1993 plh0188R 13106 2.55 0.82 0.21 0.56 1993 plh0189 13098 3.25 0.94 0.37 0.56 1993 plh0190 12198 3.20 0.87 0.35 0.56 1993 plh0191 13061 3.10 0.93 0.44 0.56 1995 plh0188R 13698 2.77 0.75 0.29 0.59 1995 plh0189 13685 3.22 0.92 0.38 0.59 1995 plh0190 12791 3.26 0.83 0.37 0.59 1995 plh0191 13653 3.21 0.87 0.45 0.59 continues on next page 3.2. Scales Manual 93 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Table 2 – continued from previous page year variable count mean sd itemrestcorr alpha 1996 plh0188R 13464 2.71 0.77 0.30 0.59 1996 plh0189 13468 3.23 0.90 0.38 0.59 1996 plh0190 12594 3.24 0.82 0.37 0.59 1996 plh0191 13429 3.20 0.84 0.45 0.59 1997 plh0188R 13227 2.50 0.79 0.25 0.57 1997 plh0189 13205 3.27 0.90 0.37 0.57 1997 plh0190 12101 3.23 0.82 0.37 0.57 1997 plh0191 13190 3.18 0.86 0.44 0.57 2008 plh0188R 19614 2.62 0.77 0.31 0.60 2008 plh0189 19608 3.16 0.88 0.40 0.60 2008 plh0190 17957 3.25 0.81 0.36 0.60 2008 plh0191 19549 3.22 0.84 0.46 0.60 2013 plh0188R 19068 2.87 0.71 0.34 0.62 2013 plh0189 19076 3.24 0.83 0.41 0.62 2013 plh0190 17163 3.29 0.83 0.39 0.62 2013 plh0191 19009 3.35 0.83 0.46 0.62 2018 plh0188R 25769 2.82 0.76 0.28 0.59 2018 plh0189 25814 3.21 0.86 0.38 0.59 2018 plh0190 22900 3.24 0.85 0.37 0.59 2018 plh0191 25709 3.30 0.83 0.44 0.59 3.2.3 Basic Social Justice Orientations Scale Summary The Basic Social Justice Orientations (BSJO) is a short scale to measure individuals’ support for equality, need, equity, and entitlement, which are four basic distributive principles in justice research (Hülle/Liebig/May 2017). After validation and use in several population surveys (LINOS-1, SOEP-IS 2012, ALLBUS 2014, ESS round nine), the module became part of the SOEP questionnaire program from 2019 onwards. Theoretical Background Starting as a equity theory (Adams 1963) the principles of justice were extended by a “multi-principle approach” in the mid-1970s, including equality and need (Deutsch 1975), and by the emphasis on entitlement (Miller 1976). These four principles of distributive justice form the four dimensions of the Basic Social Justice Orientations (BSJO) scale, which is used to analyze normative attitudes towards the resolution of distributive problems in society (Hülle/Liebig/May 2017). The dimension of equality implies a conception of justice according to which everyone receives an equal share of benefits and burdens. Within the 2nd dimension—equity—the distribution of benefits and burdens is considered equitable if these are linked to current individual contributions and efforts. The needs principle represents the 3rd dimension and corresponds to the idea of sharing benefits according to people’s individual needs. The 4th dimension is the principle of entitlement. According to this principle, ascriptive characteristics (such as social origin) or status characteristics acquired in the past (such as occupational status) form entitlements on the basis of which benefits and burdens are to be distributed. Scale Development Pretests in LINOS-1, SOEP-IS 2012 and for the main survey of ALLBUS 2014 not only verified the validity of the items, they also reduced the number of items from 12 to 8 by using the criterion of factorial validity, so that each dimension was then covered by 2 items (Hülle/Liebig/May 2017). In ALLBUS 2014 two items (“It is just if all people have the same living conditions” and “It is just if income and wealth are equally distributed among the members of our society”) measured equality, two items (“A society is just if it takes care of those who are poor and needy” and “It is just if people taking care of their children or their dependent relatives receive special support and benefits”) measured 94 Chapter 3. Survey Design SOEP Survey Paper 1261 SOEPcompanion, Release 2023 need, two items (“It is just if hard working people earn more than others” and “It is just if every person receives only that which has been acquired through their own efforts”) measured equity, and two items (“It is just if members of respectable families have certain advantages in their lives” and “It is fair if people on a higher level of society have better living conditions than those on the lower level”) measured entitlement (Sauer et al. 2014). For the European Social Survey round nine (2018/2019) previous results of the BSJO scale were used to develop and include a four-item- version with one item per dimension (Adriaans et al. 2020). In 2019, the SOEP included the ultra-brief version of with the plan to replicate them every 5 years in the questionnaire program. However, a focus module on perceptions of inequality was conducted in the SOEP 2021 (Adriaans et al. 2021). The BSJO scale was repeated in 2021 as a complement to the content of this module. References Adams, J. S. (1963). Towards an understanding of inequity. The Journal of Abnormal and Social Psychology, 67(5), 422–436. Adriaans, J., Bohmann, S., Targa, M., Liebig, S., Hinz, T., Jasso, G., ... & Sabbagh, C. (2020). Justice and fairness in europe: Topline results from round 9 of the european social survey. Adriaans, J., Griese, F., Auspurg, K., Bledow, N., Bohmann, S., Busemeyer, M. R., ... & Verwiebe, R. (2021). Dokumentation zum Entwicklungsprozess des Moduls” Einstellungen zu sozialer Ungleichheit” im SOEP (v38) (No. 1071). SOEP Survey Papers. Deutsch, M. (1975). Equity, equality, and need: What determines which value will be used as the basis of distributive justice? Journal of Social Issues, 31(3), 137–149. Hülle, S., Liebig, S., & May, M. J. (2018). Measuring attitudes toward distributive justice: The basic social justice orientations scale. Social Indicators Research, 136(2), 663-692. Miller, D. (1976). Social justice. Oxford: Clarendon Press. Valet, P., May, M., Sauer, C., & Liebig, S. (2014). LINOS-1: Legitimation of inequality over the life-span. Items People have different ideas about what makes a society just. What’s your opinion about the following statements? (Es gibt unterschiedliche Vorstellungen darüber, wann eine Gesellschaft gerecht ist. Wie ist Ihre persönliche Meinung zu den folgenden Aussagen?) 1. A society is just when people who work hard earn more than others / Es ist gerecht, wenn Personen, die hart arbeiten, mehr verdienen als andere (Equity) 2. A society is just when people from respected families have advantages in life / Es ist gerecht, wenn Personen, die aus angesehenen Familien stammen, dadurch Vorteile im Leben haben (Entitlement) 3. A society is just when it takes care of the weak and needy / Eine Gesellschaft ist gerecht, wenn sie sich um die Schwachen und Hilfsbedürftigen kümmert (Need) 4. A society is just when the income and wealth in society are equally distributed among all people. / Es ist gerecht, wenn Einkommen und Vermögen in unserer Gesellschaft an alle Personen gleich verteilt warden. (Equality) Scale: 1 (Disagree completely / Stimme überhaupt nicht zu) to 7 (Agree completely / Stimme voll zu) Items and Scale Statistics year variable count mean sd itemrestcorr alpha 2019 plh0396i01 25841 6.28 1.08 0.21 0.21 2019 plh0396i02 25792 2.09 1.45 0.12 0.21 2019 plh0396i03 25856 6.19 1.11 0.05 0.21 2019 plh0396i04 25742 3.10 1.91 0.08 0.21 3.2. Scales Manual 95 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 3.2.4 Cognitive Competencies Symbol-Digit Test and Animal Naming Task Summary The two ultra-short cognitive performance tasks allow for reliable assessment of general intellectual ability and distinguish between two components of intellectual functioning: cognitive mechanics and pragmatics (e.g., Lindenberger & Baltes, 1997). Each test takes 90 seconds and is completed on a laptop. The respondent completes the Symbol-Digit Test him/herself, whereas the interviewer documents the answers to the Animal Naming Task. Both tasks therefore require the survey mode of Computer Assisted Personal Interviewing (CAPI). Theoretical Background Cognitive mechanisms are hard-wired, biologically based capacities for information processing and are measured with the Symbol-Digit Test (SDT). The study of cognitive mechanics deals with differences in cognitive performance, for example, in the speed, accuracy, processing capacity, coordination, and inhibition of basic cognitive processes. Prime examples include perceptual speed, working memory, and the capacity for deductive reasoning. The cognitive mechanics usually develop in a process continuing up to early adulthood, then begin to decline gradually and may deteriorate more rapidly in some areas in old age. Cognitive pragmatics are education- and experience-related competencies, which are measured with the Animal Naming Task (ANT). The development of cognitive pragmatics is the result of investments in the development of cognitive mechanics in selected behavioral areas early in the life course (e.g., in educational trajectories, in training). The cognitive pragmatics develop continuously throughout life, reaching their peak late in the life course and declining only marginally in old age. Unsurprisingly, the cognitive abilities that build on pragmatic intellectual abilities (e.g., knowledge, vocabulary, wisdom) usually correlate much more strongly with socio-economic resources such as education, income, and occupational prestige, whereas the development of basic cognitive mechanisms over the life course is much more strongly affected by individual sensory and psychomotor resources (Baltes, Lindenberger & Staudinger, 1998). Scale Development The individual tests of perceptual speed (Symbol-Digit Test) and word fluency (Animal Naming Test) were modified for use with a computer-assisted survey mode. The decisive factor in this was the need to be able to use the tests without any special interviewer training and to reduce sources of error in the framework of Computer-Assisted Personal Interviewing (CAPI) as much as possible. Further information on the development of the tests can be found in Lang (2005), Lang, Weiss, Stocker and von Rosenbladt (2007), and in Schupp, Herrmann, Jaensch and Lang (2008). References Baltes, P. B., Lindenberger, U. & Staudinger, U. M. (1998). Life-span theory in developmental psychology. In R. M. Lerner (Ed.), Handbook of child psychology (5th edition, Vol. 1: Theoretical models of human development, pp. 1029 – 1143). New York: Wiley. Lang, F. R. (2005). Erfassung des kognitiven Leistungspotenzials und der “Big Five” mit Computer- Assisted-Personal- Interviewing (CAPI): Zur Reliabilität und Validität zweier ultrakurzer Tests und des BFI-S (Assessment of cognitive capabilities and the Big Five with Computer-Assisted Personal Interviewing (CAPI): Reliability and validity). German Institute of Economic Research. Berlin: DIW Berlin. Lang, F. R., Weiss, D., Stocker, A., & von Rosenbladt, B. (2007). Assessing cognitive capacities in Computer-Assisted Survey Research: Two ultra-short tests of intellectual ability in the German Socio-Economic Panel (SOEP). Schmollers Jahrbuch, 127, 183-192. Lindenberger, U., & Baltes, P. B. (1997). Intellectual functioning in old and very old age: Crosssectional results from the Berlin Aging Study. Psychology and Aging, 12, 410-432. Schupp, J., Herrmann, S., Jaensch, P., & Lang, F. R. (2008). Erfassung kognitiver Leistungspotentiale Erwachsener im Sozio-oekonomischen Panel (SOEP). Berlin: DIW Berlin. Items 96 Chapter 3. Survey Design SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Symbol-Digit Test & Animal Naming Task 1. In 30 seconds. 2. In 30-60 seconds. 3. In 60-90 seconds. Items and Scale Statistics year variable count mean sd itemrestcorr alpha 2006 f99z30r 5790 8.09 4.09 0.86 0.94 2006 f99z60rneu 5790 9.05 4.10 0.91 0.94 2006 f99z90rneu 5790 8.61 3.62 0.86 0.94 2012 f99z30r 7342 8.97 3.82 0.78 0.90 2012 f99z60rneu 7342 10.22 3.55 0.85 0.90 2012 f99z90rneu 7342 9.98 3.07 0.77 0.90 2016 f99z30r 18155 9.50 6.13 0.39 0.68 2016 f99z60rneu 18155 10.62 3.72 0.65 0.68 2016 f99z90rneu 18155 10.25 3.23 0.61 0.68 2006 f96t30g 5790 11.83 6.02 0.40 0.65 2006 f96t60gneu 5790 7.18 4.60 0.57 0.65 2006 f96t90gneu 5790 5.06 4.19 0.47 0.65 2012 f96t30g 1285 12.64 5.18 0.40 0.68 2012 f96t60gneu 1285 8.86 4.44 0.58 0.68 2012 f96t90gneu 1285 6.34 4.14 0.52 0.68 2016 f96t30g 809 11.99 4.83 0.46 0.70 2016 f96t60gneu 809 8.88 4.18 0.60 0.70 2016 f96t90gneu 809 6.53 3.93 0.52 0.70 Multiple-Choice Vocabulary Intelligence Test (MWT) Summary The Multiple-Choice Vocabulary Intelligence Test (Mehrfachwahl-Wortschatz-Intelligenztest; MWT; Lehrl, 2005) aims to measure the education- and experience-related cognitive pragmatics. The test asks for knowledge and is therefore only minimally influenced by currently availably cognitive capacities. The test takes about 5 minutes. The respondents are asked to find the existing and commonly known word in 37 groups of five words each with four words in each group being fictive and newly constructed (multiple-choice). The 37 groups are ordered by difficulty and the test is finished after three incorrect classifications. Scale Development The Multiple-Choice Vocabulary Intelligence Test (Version A) was modified for use with a computerassisted survey mode. After pretesting in 2011 the test was introduced into the SOEP in 2012 (N = 6,864, M = 28.14, SD = 6.98). References Lehrl, S. (1991). Mehrfachwahl-Wortschatz-Intelligenztest MWT-A; (Parallelform zum MWT-B). Erlangen: perimed- Fachbuch-Verl.-Ges. Items The MWT items are not allowed to be published. 3.2. Scales Manual 97 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 shapes such decisions over time. Scale Development So far, there is still no reliable measure of patience in large-scale representative surveys, since direct measures of patience are typically elicited in laboratory experiments among particular (student) subject pools only. To fill this gap, a survey measure of patience and impulsiveness has been included in the SOEP. The measures for impulsiveness and patience were evaluated with an incentive-compatible intertemporal choice experiment for impatience. Individuals who state that they are more impatient also exhibit a higher degree of impatience in the incentivized choice experiment (see Vischer et al., 2013). References Vischer, T., Dohmen, T., Falk, A., Huffman, D., Schupp, J., Sunde, U., & Wagner, G. G. (2013). Validating an ultra-short survey measure of patience. Economics Letters, 120, 142-145. Items How would you describe yourself (Wie schätzen Sie sich persönlich ein): Impulsiveness 1. Do you generally think things over for a long time before acting – in other words, are you not impulsive at all? Or do you generally act without thinking things over a long time – in other words, are you very impulsive? (Sind Sie im Allgemeinen ein Mensch, der lange überlegt und nachdenkt, bevor er handelt, also gar nicht impulsive ist? Oder sind Sie ein Mensch, der ohne lange zu überlegen handelt, also sehr impulsiv ist?) Scale: 0 (Not at all impulsive / Gar nicht impulsiv) to 10 (Very impulsive / Sehr impulsiv) Patience 2. Are you generally an impatient person, or someone who always shows great patience? (Sind Sie im Allgemeinen ein Mensch, der ungeduldig ist, oder der immer sehr viel Geduld aufbringt?) Scale: 0 (Very impatient / Sehr ungeduldig) to 10 (Very patient / Sehr geduldig) Items and Scale Statistics year variable count mean sd 2008 plh0254 19635 5.09 2.19 2008 plh0253 19643 6.07 2.28 2013 plh0254 19107 5.18 2.21 2013 plh0253 19114 6.18 2.34 2018 plh0254 25910 5.03 2.30 2018 plh0253 25936 6.00 2.45 3.2.8 Life Goals Summary Respondents’ life goals have been measured in the SOEP in 1990, 1992, 1995, since 2004 at four-year intervals and since 2016 at five-year intervals. They have been used with ten items that can be grouped into three scales (success, family life, and altruism). Theoretical Background Life goals can be thought of as “relatively longterm, value-laden life objectives” (Meier et al., 1959). They have been conceptualized as “organizers” of developmental self-regulation that individuals use to influence their own development as they adapt to the constraints of a given situation (Heckhausen, 1999). Individual life goals correspond to societal 104 Chapter 3. Survey Design SOEP Survey Paper 1261 SOEPcompanion, Release 2023 values to the extent that they are aligned with cultural preferences, if not culturally prescribed norms. A classic example is the goal of material success as embodied in the achievement of money, power, and security, which in turn lead to prestige and social recognition. However, overarching non-monetary goals such as “the good life” or “personal development” are also taking on an increasingly important role in individuals’ lives (Meier et al., 1959). Research on life goals has investigated their impact on individuals’ future orientations and their occupational, educational, and family-related decision making (Chang et al., 2006). Life goals also play a prominent role in motivational theories of life-span development, which focus on goal commitment, planning, and eventual goal attainment (Heckhausen et al., 2010). Another strand of research is concerned with the prioritization of life goals and perceived control over goal attainment (Heckhausen, 1999). Recent evidence that life goals play a role in life satisfaction has contributed to the research on subjective well-being and life satisfaction (Heady 2008). Scale Development The items are based (albeit with some changes of wording) on a classification of goals and measures initially developed by Kluckhohn and Strodtbeck (1961) and translated into German by Bielenski and Strümpel (1988). More information on the development of the scale is given by Headey (2008). References Bielenski, H. & Strümpel, B. (1988). Eingeschränkte Erwerbsarbeit bei Frauen und Männern. Fakten - Wünsche - Realisierungschancen. Berlin: Edition Sigma. Chang, E. S., Chen, C, Greenberger, E., Dooley, D., & Heckhausen, J. (2006). What do they want in life?: The life goals of a multi-ethnic, multi-generational sample of high school seniors. Journal of Youth and Adolescence, 35, 302-313. Headey, B. W. (2008). Life goals matter to happiness: A revision of set-point theory. Social Indicators Research, 86, 213-231. Heckhausen, J. (1999). Developmental regulation in adulthood: Age-normative and sociostructional constraints as adaptive challenges. New York: Cambridge University Press. Heckhausen, J., Wrosch, C., & Schulz, R. (2010). A motivational theory of life-span development. Psychological Review, 117, 32-60. Kluckhohn, F. R., & Strodtbeck, F. L. (1961). Variations in value orientations. Evanston, Illinois: Row, Peterson. Meier, D. L., & Bell, W. (1959). Anomia and differential access to the achievement of life goals. American Sociological Review, 24, 189-202. Items Are the following things currently ... for you (Sind für Sie persönlich die folgenden Dinge heute ...): Success 1. Being able to afford to buy things for myself (Sich etwas leisten können). 2. Being fullfilled (Sich selbst verwirklichen). 3. Being successful in my career (Erfolg im Beruf haben). 4. Seeing the world and/or traveling extensively (Die Welt sehen, viele Reisen machen). Scale: 1 (Very important / Sehr wichtig) to 4 (Not at all important / Ganz unwichtig) Items and Scale Statistics year variable count mean sd itemrestcorr alpha 1990 plh0104 7136 2.07 0.62 0.36 0.60 1990 plh0106 7101 2.15 0.76 0.45 0.60 1990 plh0107 6845 2.18 0.92 0.43 0.60 1990 plh0112 7143 2.47 0.84 0.31 0.60 continues on next page 3.2. Scales Manual 105 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Table 9 – continued from previous page year variable count mean sd itemrestcorr alpha 1992 plh0104 11018 1.96 0.62 0.38 0.61 1992 plh0106 10927 2.13 0.79 0.47 0.61 1992 plh0107 10672 2.11 0.95 0.44 0.61 1992 plh0112 11008 2.47 0.83 0.30 0.61 1995 plh0104 11663 1.98 0.61 0.36 0.61 1995 plh0106 11585 2.09 0.76 0.49 0.61 1995 plh0107 11200 2.07 0.90 0.45 0.61 1995 plh0112 11639 2.54 0.81 0.30 0.61 2004 plh0104 21933 1.98 0.59 0.37 0.61 2004 plh0106 21809 2.15 0.75 0.48 0.61 2004 plh0107 20840 2.13 0.87 0.43 0.61 2004 plh0112 21917 2.56 0.82 0.30 0.61 2008 plh0104 19632 2.01 0.60 0.37 0.63 2008 plh0106 19547 2.23 0.77 0.50 0.63 2008 plh0107 18612 2.21 0.90 0.45 0.63 2008 plh0112 19596 2.64 0.82 0.33 0.63 2010 plh0104 7788 2.06 0.66 0.39 0.59 2010 plh0106 7768 2.05 0.71 0.45 0.59 2010 plh0107 7785 2.06 0.71 0.41 0.59 2010 plh0112 7780 2.58 0.81 0.27 0.59 2012 plh0104 27925 2.06 0.63 0.36 0.61 2012 plh0106 27807 2.13 0.74 0.49 0.61 2012 plh0107 26216 2.16 0.86 0.44 0.61 2012 plh0112 27903 2.56 0.83 0.29 0.61 2016 plh0104 24603 2.06 0.63 0.35 0.62 2016 plh0106 24483 2.14 0.74 0.48 0.62 2016 plh0107 23229 2.20 0.85 0.45 0.62 2016 plh0112 24580 2.49 0.84 0.33 0.62 Items Are the following things currently ... for you (Sind für Sie persönlich die folgenden Dinge heute ...): Family Life 1. Owning a house (Ein eigenes Haus haben). 2. Having a happy marriage/relationship (Eine glückliche Ehe / Partnerschaft haben). 3. Having children (Kinder haben). Scale: 1 (Very important / Sehr wichtig) to 4 (Not at all important / Ganz unwichtig) Items and Scale Statistics year variable count mean sd itemrestcorr alpha 1990 plh0108 7125 2.39 0.99 0.32 0.56 1990 plh0109 7077 1.44 0.74 0.40 0.56 1990 plh0110 7067 1.88 0.88 0.42 0.56 1992 plh0108 10947 2.41 1.01 0.31 0.56 1992 plh0109 10936 1.40 0.73 0.42 0.56 1992 plh0110 10888 1.79 0.90 0.41 0.56 continues on next page 106 Chapter 3. Survey Design SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Table 10 – continued from previous page year variable count mean sd itemrestcorr alpha 1995 plh0108 11592 2.33 0.98 0.28 0.54 1995 plh0109 11568 1.39 0.71 0.40 0.54 1995 plh0110 11529 1.77 0.89 0.40 0.54 2004 plh0108 21811 2.32 0.98 0.31 0.55 2004 plh0109 21730 1.43 0.69 0.41 0.55 2004 plh0110 21572 1.80 0.89 0.39 0.55 2008 plh0108 19534 2.41 0.98 0.30 0.54 2008 plh0109 19464 1.45 0.70 0.39 0.54 2008 plh0110 19391 1.76 0.88 0.38 0.54 2010 plh0108 7786 2.28 0.92 0.24 0.47 2010 plh0109 7786 1.27 0.53 0.37 0.47 2010 plh0110 7783 1.34 0.56 0.34 0.47 2012 plh0108 27801 2.41 0.98 0.29 0.52 2012 plh0109 27663 1.45 0.70 0.38 0.52 2012 plh0110 27612 1.67 0.84 0.35 0.52 2016 plh0108 24490 2.38 0.98 0.28 0.51 2016 plh0109 24380 1.46 0.71 0.38 0.51 2016 plh0110 24331 1.65 0.84 0.34 0.51 Items Are the following things currently ... for you (Sind für Sie persönlich die folgenden Dinge heute ...): Altruism 1. Being there for others (Für andere da sein). 2. Spending a lot of time with friends (Viel mit Freunden zusammen sein). 3. Being politically and/or socially involved (Sich politisch, gesellschaftlich einsetzen). Scale: 1 (Very important / Sehr wichtig) to 4 (Not at all important / Ganz unwichtig) Items and Scale Statistics year variable count mean sd itemrestcorr alpha 1990 plh0105 7142 1.90 0.59 0.19 0.40 1990 plh0090 7136 2.13 0.71 0.27 0.40 1990 plh0111 7125 2.99 0.76 0.25 0.40 1992 plh0105 11010 1.88 0.60 0.27 0.46 1992 plh0090 10993 2.10 0.71 0.33 0.46 1992 plh0111 10953 3.16 0.73 0.26 0.46 1995 plh0105 11644 1.82 0.59 0.24 0.43 1995 plh0090 11619 2.09 0.70 0.32 0.43 1995 plh0111 11597 3.16 0.71 0.24 0.43 2004 plh0105 21926 1.83 0.58 2004 plh0111 21830 2.93 0.75 2008 plh0105 19626 1.83 0.57 2008 plh0111 19518 3.10 0.75 2010 plh0105 7788 1.51 0.55 2010 plh0111 7771 2.69 0.79 2012 plh0105 27910 1.71 0.57 continues on next page 3.2. Scales Manual 107 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Table 11 – continued from previous page year variable count mean sd itemrestcorr alpha 2012 plh0111 27853 2.81 0.77 2016 plh0105 24604 1.69 0.57 2016 plh0111 24520 2.79 0.78 3.2.9 Life Satisfaction Summary General life satisfaction has been measured with an individual item in the SOEP on an annual basis since 1984. Domainspecific life satisfaction was initially measured with seven items (1984-1990), and since 2008 with ten items. Items 1 to 5 can be combined into one scale in all survey years (Schimmack, Krause, Wagner, & Schupp, 2009). In addition, satisfaction in 11 further domains is surveyed at irregular intervals. Theoretical Background Since its inception, the SOEP has included cognitive measures of well-being. The first measure is the global 11-point rating of life satisfaction (Schimmack, Schupp, & Wagner, 2008). This item is used almost exclusively as a measure of well-being in the SOEP. The reasons for its popularity are its high face validity and the widespread use of life satisfaction ratings in the well-being literature. Single-item measures of life satisfaction are a reasonably valid and common way to measure general life satisfaction: moderate associations with other well-being measures, including written interviews, informant reports, and measures of daily affect are reported by Sandvik, Diener, and Seidlitz (1993). Research using the World Value Survey (http://www.worldvaluessurvey.org) found single-item measures of life satisfaction to be positively related to affect balance and positive affect, and inversely related to negative affect (Suh et al., 1998). The second measure is the average of various domain satisfactions that are routinely assessed in the SOEP (health, household income, dwelling, and leisure time). This measure has two drawbacks. First, it does not weigh domains by their subjective importance. Second, the measure fails to capture aspects of well-being that are not covered by the domains included in the survey (Schimmak, 2008). A key advantage of this measure is that it relies not solely on respondents’ ability to summarize and weigh all relevant aspects of their lives in response to a single question about satisfaction with life in general. Scale Development Information on the further development, reliability, and validity of the items can be found in Kroh (2006), in Schimmak (2008) and in Schimmack, Krause, Wagner, & Schupp (2009). References Kroh, M. (2006). An experimental evaluation of popular well-being measures. Berlin: DIW Berlin. Sandvik, E., Diener, E., & Seidlitz, L. (1993). Subjective well-being: The convergence and stability of self-report and non-self-report measures. Journal of Personality, 61, 317–342. Schimmack, U., Schupp, J., & Wagner, G. G. (2008). The influence of environment and personality on the affective and cognitive component of subjective well-being. Social Indicators Research, 89, 41-60. Schimmack, U., Krause, P., Wagner, G. G., & Schupp, J. (2009). Stability and change of Well Being: An experimentally enhanced Latent State-Trait-Error Analysis. Social Indicators Research, 95, 19-31. Suh, E., Diener, E., Oishi, S., & Triandis, H. C. (1998). The shifting basis of life satisfaction judgments across cultures: Emotions versus norms. Journal of Personality and Social Psychology, 74, 482–493. Items 1. How satisfied are you with your life, all things considered? (Wie zufrieden sind Sie gegenwärtig, alles in allem, mit Ihrem Leben?) How satisfied are you with (Wie zufrieden sind Sie): 2. your health (mit ihrer Gesundheit)? 108 Chapter 3. Survey Design SOEP Survey Paper 1261 SOEPcompanion, Release 2023 3. your household income (mit dem Einkommen Ihres Haushalts)? 4. your dwelling (mit Ihrer Wohnung)? 5. your free time (mit Ihrer Freizeit / in den Jahren 1995, 1996: mit Ihrer Freizeittätigkeit)? 6. your job (mit ihrer Arbeit)? 7. your housework (mit ihrer Tätigkeit im Haushalt)? 8. the child care available (mit den vorhandenen Möglichkeiten der Kinderbetreuung)? 9. your personal income (mit Ihrem persönlichen Einkommen)? 10. your family life (mit Ihrem Familienleben)? 11. your sleep (mit Ihrem Schlaf)? Scale: 0 (Completely dissatisfied / Ganz und gar unzufrieden) to 10 (Completely satisfied / Ganz und gar zufrieden) Test-Retest Correlations In 2005, 2006 and 2009, item 1 to 5 were included in retests taken by subsamples within 30 to 49 days after the initial test. Pooled across the years (minimal N = 603), test-retest correlations were (in scale order, item 1 to 5) .66, .64, .71, .67, and .56. The test-retest correlation of the scale based on these five items was .77. Items and Scale Statistics year variable count mean sd 1984 plh0182 12192 7.43 2.14 1984 plh0171 12224 7.00 2.67 1984 plh0175 12042 6.41 2.62 1984 plh0177 12167 7.60 2.54 1984 plh0178 12172 7.29 2.55 1984 plh0173 7024 7.65 2.28 1984 plh0174 7843 6.95 2.44 1985 plh0182 11050 7.24 2.05 1985 plh0171 11035 6.93 2.47 1985 plh0175 10872 6.46 2.46 1985 plh0177 10979 7.59 2.38 1985 plh0178 11006 7.14 2.44 1985 plh0173 6338 7.52 2.14 1985 plh0174 6433 6.95 2.26 1986 plh0182 10611 7.29 1.94 1986 plh0171 10618 6.89 2.44 1986 plh0175 10507 6.50 2.35 1986 plh0177 10587 7.56 2.29 1986 plh0178 10595 7.01 2.44 1986 plh0173 6169 7.41 2.12 1986 plh0174 6388 6.86 2.20 1987 plh0182 10482 7.14 1.96 1987 plh0171 10498 6.85 2.39 1987 plh0175 10400 6.52 2.28 1987 plh0177 7943 7.89 2.08 1987 plh0178 10459 7.07 2.37 1987 plh0173 6155 7.40 2.07 1987 plh0174 6363 6.91 2.11 1988 plh0182 9980 7.08 1.96 continues on next page 3.2. Scales Manual 109 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Table 12 – continued from previous page year variable count mean sd 1988 plh0171 10001 6.78 2.39 1988 plh0175 9905 6.51 2.29 1988 plh0177 9978 7.52 2.24 1988 plh0178 9978 6.92 2.37 1988 plh0173 5837 7.25 2.09 1988 plh0174 6113 6.80 2.12 1989 plh0182 9679 7.10 1.94 1989 plh0171 9683 6.71 2.42 1989 plh0175 9600 6.53 2.25 1989 plh0177 9653 7.48 2.24 1989 plh0178 9651 6.85 2.37 1989 plh0173 5692 7.24 2.03 1989 plh0174 6065 6.69 2.13 1990 plh0182 13904 7.05 1.88 1990 plh0171 13940 6.75 2.43 1990 plh0175 13813 6.27 2.32 1990 plh0177 13890 7.29 2.43 1990 plh0178 9482 7.09 2.15 1990 plh0173 9079 7.22 2.15 1990 plh0174 6393 6.69 2.13 1990 plh0179 1483 7.59 2.65 1991 plh0182 13535 6.95 1.90 1991 plh0171 13643 6.75 2.34 1991 plh0175 13531 6.19 2.40 1991 plh0177 13622 7.29 2.32 1991 plh0178 13605 6.59 2.49 1991 plh0173 8534 6.96 2.28 1992 plh0182 13307 6.92 1.82 1992 plh0171 13349 6.81 2.32 1992 plh0175 13257 6.12 2.26 1992 plh0177 13307 7.14 2.31 1992 plh0178 13314 6.64 2.38 1992 plh0173 8050 7.23 1.98 1993 plh0182 13113 6.88 1.88 1993 plh0171 13146 6.66 2.31 1993 plh0175 12831 6.15 2.30 1993 plh0177 13095 7.22 2.32 1993 plh0178 13102 6.58 2.42 1993 plh0173 7786 7.05 2.06 1993 plh0174 8623 6.51 2.12 1994 plh0182 13344 6.86 1.85 1994 plh0171 13383 6.60 2.31 1994 plh0175 13096 6.05 2.33 1994 plh0177 13333 7.30 2.27 1994 plh0178 13323 6.55 2.43 1994 plh0173 7803 7.00 2.07 1994 plh0174 8986 6.51 2.12 1995 plh0182 13696 6.89 1.83 1995 plh0171 13709 6.67 2.25 1995 plh0175 13449 6.12 2.29 continues on next page 110 Chapter 3. Survey Design SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Table 12 – continued from previous page year variable count mean sd 1995 plh0177 13626 7.29 2.24 1995 plh0178 13678 6.80 2.22 1995 plh0173 8537 6.89 2.20 1995 plh0174 9429 6.52 2.11 1996 plh0182 13489 6.90 1.78 1996 plh0171 13497 6.62 2.24 1996 plh0175 13242 6.18 2.27 1996 plh0177 13426 7.36 2.19 1996 plh0178 13455 6.79 2.33 1996 plh0173 8367 6.88 2.16 1996 plh0174 9294 6.54 2.06 1997 plh0182 13257 6.79 1.79 1997 plh0171 13250 6.59 2.21 1997 plh0175 12980 6.01 2.24 1997 plh0177 13170 7.35 2.13 1997 plh0178 13202 6.73 2.33 1997 plh0173 8073 6.85 2.12 1997 plh0174 9201 6.50 2.05 1997 plh0179 3612 6.53 2.40 1998 plh0182 14631 6.95 1.78 1998 plh0171 14615 6.65 2.23 1998 plh0175 14300 6.11 2.28 1998 plh0177 14527 7.48 2.08 1998 plh0178 14575 6.83 2.30 1998 plh0173 8762 6.89 2.15 1998 plh0174 10202 6.56 2.06 1998 plh0179 3811 6.67 2.43 1999 plh0182 14054 6.97 1.78 1999 plh0171 14044 6.61 2.24 1999 plh0175 13767 6.18 2.25 1999 plh0177 13964 7.53 2.04 1999 plh0178 13993 6.86 2.29 1999 plh0173 8555 6.90 2.12 1999 plh0174 9996 6.52 2.05 1999 plh0179 3746 6.63 2.42 2000 plh0182 24510 7.09 1.78 2000 plh0171 24534 6.76 2.27 2000 plh0175 24069 6.40 2.30 2000 plh0177 24405 7.72 2.02 2000 plh0178 24467 7.01 2.29 2000 plh0173 13485 7.05 2.20 2000 plh0174 17626 6.60 2.11 2000 plh0179 6064 6.61 2.62 2001 plh0182 22299 7.10 1.74 2001 plh0171 22270 6.77 2.24 2001 plh0175 21971 6.49 2.24 2001 plh0177 22194 7.74 1.95 2001 plh0178 22244 7.02 2.25 2001 plh0173 13262 7.06 2.16 2001 plh0174 16297 6.62 2.08 continues on next page 3.2. Scales Manual 111 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Table 12 – continued from previous page year variable count mean sd 2001 plh0179 4482 6.54 2.70 2002 plh0182 23844 7.05 1.74 2002 plh0171 23838 6.75 2.21 2002 plh0175 23443 6.50 2.22 2002 plh0177 23723 7.78 1.91 2002 plh0178 23770 7.00 2.20 2002 plh0173 14486 7.04 2.14 2002 plh0174 17140 6.57 2.06 2002 plh0179 4257 6.53 2.54 2003 plh0182 22568 6.96 1.78 2003 plh0171 22571 6.73 2.19 2003 plh0175 22229 6.34 2.29 2003 plh0177 22494 7.76 1.92 2003 plh0178 22522 7.02 2.19 2003 plh0173 13465 6.96 2.17 2003 plh0174 16753 6.59 2.02 2003 plh0179 9420 6.33 2.47 2004 plh0182 21964 6.80 1.82 2004 plh0171 21940 6.61 2.24 2004 plh0175 21522 6.22 2.30 2004 plh0177 21858 7.85 1.86 2004 plh0178 21873 6.98 2.18 2004 plh0173 12964 6.87 2.21 2004 plh0174 16460 6.56 1.99 2004 plh0179 3986 6.35 2.59 2004 plh0176 21197 5.65 2.60 2005 plh0182 21040 6.95 1.83 2005 plh0171 21041 6.64 2.24 2005 plh0175 20710 6.25 2.34 2005 plh0177 20968 7.82 1.89 2005 plh0178 20993 6.98 2.24 2005 plh0173 12329 6.85 2.22 2005 plh0174 15904 6.58 2.03 2005 plh0179 3800 6.30 2.74 2005 plh0176 20340 5.65 2.66 2006 plh0182 22284 6.91 1.80 2006 plh0171 22306 6.64 2.23 2006 plh0175 21975 6.23 2.33 2006 plh0177 22230 7.81 1.89 2006 plh0178 22254 6.98 2.25 2006 plh0173 12865 6.89 2.19 2006 plh0174 17048 6.62 2.02 2006 plh0179 3650 6.37 2.71 2006 plh0176 21678 5.66 2.62 2006 plh0180 21971 7.71 1.98 2007 plh0182 20834 6.95 1.78 2007 plh0171 20843 6.58 2.20 2007 plh0175 20560 6.26 2.27 2007 plh0177 20761 7.82 1.82 2007 plh0178 20793 6.96 2.21 continues on next page 112 Chapter 3. Survey Design SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Table 12 – continued from previous page year variable count mean sd 2007 plh0173 12260 6.85 2.16 2007 plh0174 16068 6.64 1.98 2007 plh0179 3485 6.40 2.70 2007 plh0176 20286 5.63 2.59 2007 plh0180 20523 7.68 1.97 2008 plh0182 19643 6.98 1.75 2008 plh0171 19655 6.56 2.18 2008 plh0175 19361 6.29 2.28 2008 plh0177 19598 7.96 1.77 2008 plh0178 19625 7.08 2.13 2008 plh0173 11644 6.90 2.10 2008 plh0174 15114 6.68 1.94 2008 plh0179 2924 6.78 2.53 2008 plh0176 19131 5.70 2.56 2008 plh0180 19526 7.80 1.99 2008 plh0172 19652 6.82 2.26 2009 plh0182 20733 6.98 1.78 2009 plh0171 20760 6.57 2.23 2009 plh0175 20484 6.40 2.30 2009 plh0177 20701 7.91 1.82 2009 plh0178 20728 7.13 2.15 2009 plh0173 12323 6.88 2.21 2009 plh0174 16271 6.78 1.96 2009 plh0179 3121 6.78 2.76 2009 plh0176 20360 5.79 2.61 2009 plh0180 20650 7.84 1.99 2009 plh0172 20759 6.83 2.27 2010 plh0182 26674 7.25 1.74 2010 plh0171 26678 6.80 2.24 2010 plh0175 26387 6.35 2.36 2010 plh0177 26623 7.79 1.98 2010 plh0178 26643 6.93 2.25 2010 plh0173 16095 7.01 2.24 2010 plh0174 21913 6.82 2.00 2010 plh0179 7397 7.05 2.68 2010 plh0176 26119 5.74 2.69 2010 plh0180 26545 8.00 1.95 2010 plh0172 26676 6.83 2.31 2011 plh0182 26131 7.18 1.74 2011 plh0171 26127 6.73 2.24 2011 plh0175 25821 6.45 2.34 2011 plh0177 26071 7.79 1.95 2011 plh0178 18427 7.17 2.16 2011 plh0173 16459 7.07 2.17 2011 plh0174 21705 6.79 1.96 2011 plh0179 7313 7.41 2.42 2011 plh0176 25225 5.91 2.62 2011 plh0180 25888 8.01 1.90 2011 plh0172 26134 6.81 2.30 2012 plh0182 26733 7.19 1.74 continues on next page 3.2. Scales Manual 113 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Table 14 – continued from previous page year variable count mean sd itemrestcorr alpha 2016 plh0379_v2 3920 5.60 1.73 0.05 0.50 2016 plh0378_v2R 3737 3.75 2.13 0.21 0.50 2016 plh0380_v2R 3981 3.38 2.12 0.26 0.50 2016 plh0386_v2R 4011 5.30 2.10 0.30 0.50 2016 plh0383_v2R 3899 4.50 2.13 0.33 0.50 2016 plh0384_v2R 3763 2.87 1.78 0.19 0.50 2016 plh0381_v2R 3924 4.20 2.09 0.31 0.50 2020 plh0379_v2 25993 5.63 1.22 0.26 0.68 2020 plh0378_v2R 25558 4.94 1.74 0.42 0.68 2020 plh0380_v2R 25913 4.51 1.62 0.36 0.68 2020 plh0386_v2R 25932 5.03 1.65 0.46 0.68 2020 plh0383_v2R 25886 4.72 1.64 0.41 0.68 2020 plh0384_v2R 25797 3.62 1.47 0.30 0.68 2020 plh0381_v2R 25934 5.29 1.49 0.53 0.68 Note In 1999 a different scale format was used. Therefore, means and standard deviations cannot be compared without transformations. Although the questionnaire contains 10 items, only a subset of 7 items can be aggregated into a scale with acceptable internal consistency (cf. Specht et al., 2013). Scale is scored so that higher values indicate an internal locus of control. 3.2.11 Loneliness Summary Loneliness has been measured in the SOEP since 2013 in four-year intervals using the 3-item short version of the UCLA loneliness scale developed by Hughes et al. (2004). In addition, loneliness has been assessed in both waves of the soep-cov survey. Theoretical Background Loneliness describes the perceived discrepancy between one’s desired and one’s actual relationships, in quantity and especially in quality (Peplau & Perlman 1982). Thus, loneliness is subjective and different from objective measures of social isolation. Loneliness increases the risk for morbidity and all-cause mortality (Hawkley & Cacioppo, 2010; Luo, Hawkley, Waite, & Cacioppo, 2012). In fact, according to a meta-analysis, the mortality risk of lonely people increases by 26 percent (Holt-Lunstad, Smith, Baker, Harris, & Stephenson, 2015). For these reasons, loneliness has been termed a major health risk by the world health organization and research on loneliness is of major interest to many disciplines in the social sciences. Scale Development The SOEP assesses loneliness using the 3-item short version of the UCLA loneliness scale (Russel, 1996) developed by Hughes et al (2004). The items were translated by into German by an bilingual expert. Details on the scale development and validation can be found in Hughes et al. (2004). References Hawkley, L. C., & Cacioppo, J. T. (2010). Loneliness matters: A theoretical and empirical review of consequences and mechanisms. Annals of Behavioral Medicine, 40(2), 218-227. Holt-Lunstad, J., Smith, T. B., Baker, M., Harris, T., & Stephenson, D. (2015). Loneliness and social isolation as risk factors for mortality: a meta-analytic review. Perspectives on Psychological Science, 10(2), 227-237. 120 Chapter 3. Survey Design SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Hughes, M. E., Waite, L. J., Hawkley, L. C., & Cacioppo, J. T. (2004). A short scale for measuring loneliness in large surveys: Results from two population-based studies. Research on Aging, 26(6), 655-672. Luo, Y., Hawkley, L. C., Waite, L. J., & Cacioppo, J. T. (2012). Loneliness, health, and mortality in old age: A national longitudinal study. Social Science & Medicine, 74(6), 907-914. Peplau, L. A., & Perlman, D. (1982). Perspectives on loneliness. In L. A. Peplau & D. Perlman (Eds.), Loneliness: A sourcebook of current theory, research and therapy (pp. 1-20). New York: John Wiley & Sons. Russell, D. W. (1996). UCLA Loneliness Scale (Version 3): Reliability, validity, and factor structure. Journal of Personality Assessment, 66(1), 20-40. Items How often do you feel... (Wie oft haben Sie das Gefühl, ...) 1. that you lack companionship? (... dass Ihnen die Gesellschaft anderer fehlt?) 2. left out? (... außen vor zu sein?) 3. isolated from others? (... dass Sie sozial isoliert sind?) Answers: 1 = very often (Sehr oft), 2 = often (Oft), 3 = sometimes (Manchmal), 4 = seldom (Selten), 5 = never (Nie); all items need to be reversed before forming the composite score. Items and Scale Statistics year variable count mean sd itemrestcorr alpha 2013 plj0587 25762 3.62 0.94 0.56 0.78 2013 plj0588 25684 3.96 0.89 0.68 0.78 2013 plj0589 25738 4.37 0.89 0.61 0.78 2016 plj0587 4281 3.07 1.34 0.54 0.78 2016 plj0588 4218 3.56 1.27 0.68 0.78 2016 plj0589 4260 3.67 1.31 0.62 0.78 2017 plj0587 29718 3.61 1.00 0.56 0.79 2017 plj0588 29632 3.92 0.96 0.68 0.79 2017 plj0589 29665 4.30 0.96 0.64 0.79 2018 plj0587 424 3.16 1.35 0.60 0.81 2018 plj0588 424 3.66 1.30 0.73 0.81 2018 plj0589 426 3.85 1.27 0.66 0.81 2019 plj0587 277 2.96 1.27 0.44 0.71 2019 plj0588 276 4.00 1.12 0.60 0.71 2019 plj0589 277 4.03 1.13 0.57 0.71 3.2.12 Narcissistic Admiration and Rivalry Questionnaire Short Scale (NARQ-S) Summary The Narcissistic Admiration and Rivalry Questionnaire Short Scale (NARQ-S) was developed to assess the agentic (admiration) and antagonistic (rivalry) aspects of narcissism (Back et al., 2013). It is included in the SOEP in 2018 and 2023 at five-year intervals. Theoretical Background Narcissism is best understood as a multidimensional construct, incorporating agentic aspects such as dominance, charm, self-assuredness, and humor as well as antagonistic aspects such as selfishness, hostility, entitlement, and arrogance. The narcissistic admiration and rivalry concept (NARC) is a self-regulatory process model of grandiose narcissism that differentiates these two interrelated dimensions of narcissism: narcissistic admiration (agentic aspects driven by 3.2. Scales Manual 121 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 self-enhancement) and narcissistic rivalry (antagonistic aspects driven by self-defense). The Narcissistic Admiration and Rivalry Questionnaire (NARQ) assesses the agentic (admiration) and antagonistic (rivalry) aspects of narcissism according to the NARC and was developed and validated by Back et al. (2013) The NARQ-S consists of six items, three for each dimension, that can be answered on a six-point Likert-type scale ranging from 1 (not at all) to 6 (agree completely). Each dimension has three subscales that are measured by one item each and contain content addressing narcissists’ affective–motivational, cognitive, and behavioral processes. For admiration, these subscales are grandiosity, strive for uniqueness, and charmingness. The rivalry dimension consists of the subscales devaluation, strive for supremacy, and aggressiveness. A total score of the NARQ-S can be calculated in cases where researchers are interested in a global assessment of grandiose narcissism. However, calculating and using the admiration and rivalry dimensions of NARQ-S is recommended because they provide a nuanced insight into the (at times paradoxical) effects of narcissism. Scale Development For each dimension of the NARQ-S, item inclusion was based on the highest factor loading of the respective subscale of the NARQ (Back et al., 2013). A validation study by Leckelt et al. (2018) used data from a large convenience sample (total N = 11,937) as well as data from a large representative sample (total N = 4,433) that included responses to other narcissism measures as well as related constructs, including the other Dark Triad traits, Big Five personality traits, and self-esteem. The study sought to validate the factor structure, provide representative descriptive data and reliability estimates, assess the reliability across the trait spectrum, and examine the nomological network of the NARQ-S. Results suggest that the NARQ-S shows a robust factor structure and is a reliable and valid short measure of the agentic and antagonistic aspects of grandiose narcissism. The SOEP has included the NARQ-S with both subscales in its original form. It has been included in 2018 and 2023 at five-year intervals. References Back, M. D., Küfner, A. C. P., Dufner, M., Gerlach, T. M., Rauthmann, J. F., & Denissen, J. J. A. (2013). Narcissistic admiration and rivalry: Disentangling the bright and dark sides of narcissism. Journal of Personality and Social Psychology, 105, 1013–1037. Leckelt, M., Wetzel, E., Gerlach, T. M., Ackerman, R.A., Miller, J.D., Chopik, W.J., Penke, L., Geukes, K., Küfner, A. C. P., Hutteman, R., Richter, D., Renner, K.-H., Allroggen, M., Brecheen, C., Campbell, W. K., Grossmann, I. & Back, M. D. (2018). Validation of the Narcissistic Admiration and Rivalry Questionnaire Short Scale (NARQ-S) in convenience and representative samples. Psychological Assessment, 30, 86-96. Items Please indicate how much the following statements apply to you using a response format ranging from “1 = not agree at all” to “6 = agree completely”. (Wie sehr treffen die folgenden Aussagen auf Sie zu? Antworten Sie bitte anhand der folgenden Skala. Der Wert 1 bedeutet: trifft überhaupt nicht zu, der Wert 6 bedeutet: trifft vollkommen zu. Mit den Werten dazwischen können Sie Ihre Einschätzung abstufen.) 1. Being a very special person gives me a lot of strength / Ich ziehe viel Kraft daraus, eine ganz besondere Person zu sein (ADM-1) 2. I manage to be the center of attention with my outstanding contributions / Mit meinen besonderen Beiträgen schaffe ich es, im Mittelpunkt zu stehen (ADM-2) 3. I react annoyed if another person steals the show from me / Ich reagiere genervt, wenn eine andere Person mir die Schau stiehlt (RIV-1) 4. I deserve to be seen as a great personality / Ich habe es verdient, als große Persönlichkeit angesehen zu werden (ADM-3) 5. I want my rivals to fail / Ich will, dass meine Konkurrenten scheitern (RIV-2) 6. Most people are somehow losers / Die meisten Menschen sind ziemliche Versager (RIV-3) Scale: 1 (not agree at all / trifft überhaupt nicht zu) to 6 (agree completely / trifft vollkommen zu) Items and Scale Statistics 122 Chapter 3. Survey Design SOEP Survey Paper 1261 SOEPcompanion, Release 2023 year variable count mean sd itemrestcorr alpha 2018 plh0361 25187 2.60 1.50 0.57 0.78 2018 plh0362 25192 2.40 1.35 0.59 0.78 2018 plh0363 25283 1.76 1.10 0.52 0.78 2018 plh0364 25128 2.07 1.30 0.62 0.78 2018 plh0365 25169 1.61 1.08 0.45 0.78 2018 plh0366 25124 1.86 1.11 0.39 0.78 3.2.13 Optimism/Pessimism – Attitudes toward the Future Summary The SOEP measured respondents’ attitudes toward the future with an individual item in 1999, 2005, 2009, 2014, and 2019. Theoretical Background For pragmatic reasons and to save time, surveys usually measure attitudes about the future and future orientations by asking respondents whether they see the future positively or negatively, that is, optimistically or pessimistically. In the analysis of attitudes toward the future, questions on the nature and emergence of future orientations are of interest (e.g., whether attitudes about the future are less positive in adults than in young people, or whether young women are less optimistic about the future than young men; Trommsdorff, 1994). Furthermore, making assumptions about the future makes it possible to plan and anticipate one’s future actions. Attitudes about the future should therefore provide the basis for decisions and planning behavior. Scale Development The question about attitudes toward the future was included in the SOEP on the recommendation of Gisela Trommsdorff. Additional information on the validity of this individual item can be found in Trommsdorff (1994). References Trommsdorff, G. (1994). Zukunft als Teil individueller Handlungsorientierungen. In E. Holst, J. P. Rinderspacher & J. Schupp (Hrsg.), Erwartungen an die Zukunft. Zeithorizonte und Wertewandel in der sozialwissenschaftlichen Diskussion (pp. 45-76). Frankfurt a. M.: Campus. Items When you think about the future (Wenn Sie an die Zukunft denken): 1. Are you ... (Sind Sie da ...) Scale: 1 (optimistic / optimistisch) to 4 (pessimistic / pessimistisch) Test-Retest Correlations In 2009, this item was included in a retest taken by a subsample (N = 174) within 30 to 49 days after the initial test. Test-retest correlation was .60. Items and Scale Statistics year variable count mean sd 1999 plh0244 13989 1.99 0.80 2005 plh0244 20981 2.17 0.81 2009 plh0244 20699 2.23 0.81 2014 plh0244 27275 1.89 0.75 continues on next page 3.2. Scales Manual 123 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Table 17 – continued from previous page year variable count mean sd 2019 plh0244 25816 1.94 0.75 3.2.14 Parenting Goals Summary The questions on parenting goals were taken from Kohn (1977). The 18 items can be combined into two scales: “autonomy” and “conformity.” Parenting goals have been measured in the SOEP since 2010 in the questionnaire for parents of children between the ages of 7 and 8. Theoretical Background Kohn (1977) found an association between parenting goals (conformity and autonomy) and the parents’ social class: Working-class parents emphasized discipline, manners, cleanliness, good behavior in school, honesty, and obedience, whereas middle-class parents emphasized consideration, interest in why and how things happen, responsibility, and self-control. This suggests that lower-class parents place more value on conformity and higher-class parents place more value on personal autonomy. The different values of parents from different social classes regarding childrearing are transferred to their children through their own behavior and shape their children’s social character. Scale Development The scale measuring parenting goals was taken from the German General Social Survey (ALLBUS). Further information on the scale can be found in Terwey (2000). References Kohn, M. (1977). Class and conformity: A study in values. Chicago: University of Chicago Press. Terwey, M. (2000). ALLBUS: A German General Social Survey. Schmollers Jahrbuch, 120, 151-158. Items In the following, we will list a few traits and abilities that parents can foster in their children through their approach to parenting. How important do you consider the following parenting goals? That the child... (Im Folgenden werden einige Eigenschaften und Fähigkeiten genannt, die man durch Erziehung fördern kann. Für wie wichtig halten Sie persönlich die folgenden Erziehungsziele? Dass das Kind...) Autonomy / Selbstständigkeit 1. Is interested in how and why things happen (sich dafür interessiert, wie und warum bestimmte Dinge passieren) 2. Is honest (ehrlich ist) 3. Is responsible (verantwortungsbewusst ist) 4. Has good judgment (ein gutes Urteilsvermögen besitzt) 5. Strives to achieve his/her goals (sich bemüht, seine Ziele zu erreichen) 6. Learns to overcome obstacles in life (lernt, sich im Leben auch gegen Widerstände durchzusetzen). Scale: 1 (Not at all important / Überhaupt nicht wichtig) to 5 (Very important / Sehr wichtig) Obedience / Conformity (Gehorsam / Konformität) 1. Is good in school (ein guter Schüler wird) 2. Gets along with other children (sich gut mit anderen Kindern versteht) 3. Behaves like normal girl/boy (sich wie ein normales Mädchen bzw. wie ein normaler Junge verhält) 124 Chapter 3. Survey Design SOEP Survey Paper 1261 SOEPcompanion, Release 2023 4. Has good manners (gute Umgangsformen hat) 5. Has good self-control (Selbstbeherrschung besitzt) 6. Is considerate of others (auf andere Rücksicht nimmt) 7. Obeys his/her parents (seinen Eltern gehorcht) 8. Is neat and clean (ordentlich und sauber ist) 9. Fits in well in groups (sich gut in Gruppen einfügen kann) 10. Is satisfied with his/her own abilities (zufrieden mit dem ist, was es hat und kann) 11. Learns to avoid risks in life (lernt, Risiken im Leben zu meiden) 12. Is liked by others, friendly (von anderen gemocht wird, liebenswert ist) Scale: 1 (not at all important / überhaupt nicht wichtig) to 5 (very important / sehr wichtig) Items and Scale Statistics year variable count mean sd itemrestcorr alpha 2010-2020 edgoal3 14854 4.50 0.60 0.44 0.77 2010-2020 edgoal5 14879 4.82 0.42 0.43 0.77 2010-2020 edgoal8 14879 4.63 0.53 0.59 0.77 2010-2020 edgoal11 14842 4.44 0.61 0.57 0.77 2010-2020 edgoal13 14878 4.54 0.56 0.55 0.77 2010-2020 edgoal15 14855 4.53 0.59 0.53 0.77 2010-2020 edgoal1 14896 4.37 0.67 0.52 0.86 2010-2020 edgoal2 14903 4.56 0.55 0.45 0.86 2010-2020 edgoal4 14814 4.10 0.96 0.56 0.86 2010-2020 edgoal6 14890 4.57 0.57 0.57 0.86 2010-2020 edgoal7 14868 4.36 0.65 0.61 0.86 2010-2020 edgoal9 14869 4.58 0.56 0.48 0.86 2010-2020 edgoal10 14862 4.29 0.70 0.60 0.86 2010-2020 edgoal12 14878 4.24 0.70 0.64 0.86 2010-2020 edgoal14 14861 4.27 0.65 0.60 0.86 2010-2020 edgoal16 14854 4.39 0.72 0.43 0.86 2010-2020 edgoal17 14825 3.72 1.02 0.59 0.86 2010-2020 edgoal18 14859 3.97 0.80 0.60 0.86 3.2.15 Parenting Role Summary The questions on the parenting role were taken from the Panel Analysis of Intimate Relationships and Family Dynamics (pairfam) project’s parenting questionnaire (Wendt et. al., 2011). The ten items can be combined into three scales: “autonomy,” “hostile attributions,” and “willingness to make sacrifices.” The questions on the parenting role have been asked in the SOEP since 2010 in the questionnaire for parents of children aged 7-8 years. Theoretical Background The four items assessing “autonomy in the parenting role” are based on an instrument developed by Skinner and Regan (1992). The negative items measure the parents’ feelings of irksome dependence in their interaction with the child. Furthermore, it is assumed that the feeling of autonomy in the parenting role is also expressed as positive feelings towards the child. The three items of the scale “hostile attributions” are newly developed by pairfam and measure the parental disposition to interpret the child’s behavior as intentionally hostile or egoistic. The scale “willingness to 3.2. Scales Manual 125 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 make sacrifices” is an adapted version from the AGAPE scale developed by Bierhoff, Grau, & Ludwig (1993) to assess parents’ willingness to make sacrifices in their relationship with their child. Scale Development Information on the scale development can be found in the documentation provided by pairfam (Wendt et. al., 2011). References Bierhoff, H. W., Grau, I., & Ludwig, A. (1993). Marburger Einstellungsinventar für Liebesstile (MEIL). Göttingen: Hogrefe. Skinner, E. A., & Regan, C. (1992). Parenting sense of autonomy. Technical Report. University of Rochester, Rochester, NY. Wendt, E.-V., Schmahl, F., Thönnissen, C., Schaer, M., & Walper, S.(2011). Scales Manual of the German Family Panel. pairfam (Panel Analysis of Intimate Relationships and Family Dynamics). Bremen, Chemnitz, Munich. Items Autonomy 1. I have the feeling that taking care of my child/my children takes up all my strength and that my whole life revolves around it (R) (Ich habe das Gefühl, dass Betreuung und Erziehung meines Kindes mich völlig in Beschlag nehmen, mein ganzes Leben bestimmen). 2. I wish I didn’t feel so trapped by my parental duties (R) (Ich wünschte, ich würde mich durch meine Elternpflichten nicht so gefangen fühlen). 3. When I am with my child/children there is nothing else I’d rather be doing (Wenn ich mit meinem Kind zusammen bin, gibt es nichts anderes, was ich lieber täte). 4. I look forward to spending time with my child/children (Ich freue mich darauf, mit meinem Kind zusammen zu sein). Scale: 1 (Not at all / stimme überhaupt nicht zu) to 5 (Absolutely / stimme voll und ganz zu) Hostile Attributions 1. When my child disobeys and breaks rules, he/she just wants to annoy me (Wenn mein Kind nicht gehorcht und etwas Verbotenes tut, will es mich ärgern). 2. If there are any problems with the way I raise my child, then it’s my child’s fault (Wenn es Probleme in der Erziehung gibt, liegt das an meinem Kind). 3. It seems to me that when my child misbehaves, he/she does it intentionally (Ich denke, wenn mein Kind sich falsch verhält, macht es das mit Absicht). Scale: 1 (Not at all / stimme überhaupt nicht zu) to 5 (Absolutely / stimme voll und ganz zu) Readiness to Make Sacrifices 1. I am usually willing to sacrifice my own desires to satisfy those of my child (Gewöhnlich bin ich bereit, meine eigenen Wünsche denen meines Kindes zu opfern). 2. I would put up with anything for the good of my child (Ich würde alles aushalten für das Wohl meines Kindes). 3. I often stop what I am doing to offer help to my child (Ich lasse oft alles stehen und liegen, um mein Kind zu unterstützen). Scale: 1 (Not at all / Stimme überhaupt nicht zu) to 5 (Absolutely / Stimme voll und ganz zu) Items and Scale Statistics 126 Chapter 3. Survey Design SOEP Survey Paper 1261 SOEPcompanion, Release 2023 year variable count mean sd itemrestcorr alpha 2010-2020 bepar3R 14814 3.57 1.14 0.37 0.52 2010-2020 bepar4R 14778 3.89 1.05 0.49 0.52 2010-2020 bepar6 14834 3.88 0.99 0.16 0.52 2010-2020 bepar10 14868 4.63 0.59 0.28 0.52 2010-2020 bepar2 14829 2.03 1.06 0.47 0.64 2010-2020 bepar5 14809 1.59 0.81 0.38 0.64 2010-2020 bepar8 14825 1.73 0.87 0.53 0.64 2010-2020 bepar1 14849 3.98 0.88 0.45 0.65 2010-2020 bepar7 14828 4.32 0.85 0.47 0.65 2010-2020 bepar9 14856 3.72 0.97 0.46 0.65 3.2.16 Parenting Style Summary The questions on the parenting style were taken from the Panel Analysis of Intimate Relationships and Family Dynamics (pairfam) project’s parenting questionnaire (Wendt et. al., 2011). The 18 items can be combined into six scales: “emotional warmth,” “inconsistent parenting,” “monitoring,” “negative communication,” “psychological control,” and “strict control.” The questions on parenting style have been asked in the SOEP since 2010 in the questionnaire for parents of children aged 7-8 years. Theoretical Background The scale “emotional warmth” comprises three items indicating the degree of affirmative attention and care in parenting. The items are based on mothers’ and fathers’ actual parenting behavior (see the corresponding scale of Jaursch, 2003, based on Perris et al., 1980, and Schuhmacher, Eisemann, & Brähler, 1999). The scale “inconsistent parenting” comprises three items indicating the degree of inconsistent behavior in parenting. The items are based on a questionnaire on parenting from Reichle and Franiek (2005). The scale “monitoring” comprises three items indicating the degree to which parents are informed about their child’s activities and social contacts. The items are based on a questionnaire on parenting from Reichle and Franiek (2005). The scale “negative communication” comprises three items indicating the degree to which parents behave negatively toward their child. The items are based on the instrument developed by Schwarz, Walper, Gödde, and Jurasic (1997). The scale “psychological control” consists of three items. The items assess negative intrusive thoughts, feelings, and behavior of parents toward their child and are a shortened and adapted version of the scale “psychological pressure” from the “Zurich Brief Questionnaire for the Assessment of Parental Behaviors” (Reitzle et al. 2001). The scale “strict control” comprises three items indicating harsh control and authoritarian behavior of parents. The items are based on the instrument of Schwarz et al. (1997). Scale Development Information on the scale development can be found in the documentation of pairfam (Wendt et. al., 2011). References Perris, C., Jacobsson, L., Lindström, H., von Knorring, L., & Perris, H. (1980). Development of a new inventory for assessing memories of parental rearing behaviour. Acta Psychiatrica Scandinavica, 61, 265-274. Jaursch, Stefanie, 2003. Erinnertes und aktuelles Erziehungsverhalten von Müttern und Vätern: Intergenerationale Zusammenhänge und kontextuelle Faktoren. Dissertation. Friedrich- Alexander-Universität Erlangen-Nürnberg. Reichle, B., & Franiek, S. (2005). Erziehungsstil aus Elternsicht - Erweiterte deutsche Version des Alabama Parenting Questionnaire (EDAPQ) (Expanded GermanVersion of the Alabama Parenting Questionnaire). Hochschule Ludwigsburg: Institut für Pädagogische Psychologie und Soziologie. Reitzle, M., Winkler Metzke, C., & Steinhausen, H.-C. (2001). Eltern und Kinder: Der Zürcher Kurzfragebogen zum Erziehungsverhalten (Zurich Brief Questionnaire for the Assessment of Parental Behaviors). Diagnostica, 47, 196-207. 3.2. Scales Manual 127 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Schumacher, J., Eisemann, M., & Brähler, E. (1999). Rückblick auf die Eltern: Der Fragebogen zum erinnerten elterlichen Erziehungsverhalten (FEE). Diagnostika, 45, 194-204. Schwarz, B., Walper, S., Gödde, M., & Jurasic, S. (1997). Dokumentation der Erhebungsinstrumente der 1. Haupterhebung (überarb. Version). Berichte aus der Arbeitsgruppe “Familienentwicklung nach der Trennung,” 14. Wendt, E.-V., Schmahl, F., Thönnissen, C., Schaer, M., & Walper, S.). Scales Manual of the German Family Panel. pairfam (Panel Analysis of Intimate Relationships and Family Dynamics). Bremen, Chemnitz, Munich. Items Emotional Warmth 1. I show my child with words and gestures that I care about him/her (Ich zeige meinem Kind mit Worten und Gesten, dass ich es gerne habe). 2. I console child up when he/she is sad (Ich tröste mein Kind, wenn es traurig ist). 3. I praise my child (Ich lobe mein Kind). Scale: 1 (Never / nie) to 5 (Frequently / sehr häufig) Inconsistent Parenting 1. I reduce punishments or end them early (Ich schwäche eine Bestrafung ab oder hebe sie vorzeitig auf). 2. I threaten my child with a punishment but don’t actually follow through (Ich drohe meinem Kind eine Strafe an, bestrafe es aber dann doch nicht). 3. I find it hard to set and keep consistent rules for my child (Es fällt mir schwer in meiner Erziehung konsequent zu sein). Scale: 1 (Never / nie) to 5 (Frequently / sehr häufig) Monitoring 1. I try to actively influence my child’s circle of friends (Ich versuche den Freundeskreis meines Kindes aktiv zu beeinflussen). 2. When my child goes out, I ask what he/she did and experienced (Wenn mein Kind unterwegs war, frage ich nach, was es getan und erlebt hat). 3. When my child goes out, I know exactly where he/she is (Wenn mein Kind außer Haus ist, weiß ich genau, wo es sich aufhält). Scale: 1 (Never / nie) to 5 (Frequently / sehr häufig) Negative Communication 1. I criticize my child (Ich kritisiere mein Kind). 2. I yell at my child when he/she does something wrong (Ich schreie mein Kind an, wenn es etwas falsch gemacht hat). 3. I scold my child when I am angry at him/her (Ich beschimpfe mein Kind, weil ich wütend auf es bin). Scale: 1 (Never / nie) to 5 (Frequently / sehr häufig) Psychological Control 1. I am disappointed and sad when my child misbehaves (Ich bin enttäuscht und traurig, wenn sich mein Kind schlecht benommen hat). 2. I think my child is ungrateful when he/she does not obey me (Ich halte mein Kind für undankbar, wenn es mir nicht gehorcht). 128 Chapter 3. Survey Design SOEP Survey Paper 1261 SOEPcompanion, Release 2023 3. I don’t talk to my child for a while when he/she does something wrong (Ich rede eine Zeit lang nicht mit meinem Kind, wenn es etwas angestellt hat). Scale: 1 (Never / nie) to 5 (Frequently / sehr häufig) Strict Control 1. I tend to be a strict parent (Ich bin eher streng zu meinem Kind). 2. If my child does something against my will, I punish him/her (Wenn mein Kind etwas gegen meinen Willen tut, bestrafe ich es). 3. I make it clear to my child that he/she is not to break my rules or question my decisions (Ich gebe meinem Kind zu verstehen, dass es sich meinen Anordnungen und Entscheidungen nicht widersetzen soll). Scale: 1 (Never / nie) to 5 (Frequently / sehr häufig) Items and Scale Statistics year variable count mean sd itemrestcorr alpha 2010-2020 edbeh1 14673 4.47 0.64 0.55 0.73 2010-2020 edbeh8 14652 4.50 0.68 0.54 0.73 2010-2020 edbeh13 14634 4.36 0.66 0.56 0.73 2010-2020 edbeh5 14635 2.47 1.00 0.55 0.69 2010-2020 edbeh16 14518 2.66 0.98 0.50 0.69 2010-2020 edbeh18 14617 2.47 0.95 0.47 0.69 2010-2020 edbeh3 14653 4.34 0.71 0.29 0.36 2010-2020 edbeh6 14636 4.45 0.78 0.27 0.36 2010-2020 edbeh15 14604 2.18 1.05 0.11 0.36 2010-2020 edbeh2 14649 3.08 0.73 0.35 0.62 2010-2020 edbeh9 14654 2.26 0.83 0.51 0.62 2010-2020 edbeh14 14627 1.85 0.84 0.44 0.62 2010-2020 edbeh10 14600 1.54 0.76 0.38 0.51 2010-2020 edbeh11 14636 1.48 0.80 0.31 0.51 2010-2020 edbeh17 14610 2.69 0.91 0.29 0.51 2010-2020 edbeh4 14645 2.63 0.81 0.42 0.55 2010-2020 edbeh7 14627 2.95 0.90 0.33 0.55 2010-2020 edbeh12 14574 2.95 1.00 0.33 0.55 3.2.17 Patient Health Questionnaire – 4 (PHQ-4) Summary The Patient Health Questionnaire-4 (PHQ-4) is an ultra-brief screening instrument to assess the two leading symptoms of a depression and anxiety (Kroenke et al., 2009). It is included in the SOEP in its original form in 2016 and since 2019 at two-year intervals. Theoretical Background The Patient Health Questionnaire-4 (PHQ-4) is an ultra-brief screening instrument to assess the two leading symptoms of a depression and anxiety. It was developed and validated by Kroenke et al. (2009) to account for the fact that those two mental disorders are the most prevalent mental disorders in the general population, both are often comorbid, and patients that are affected by either or both of the disorders often lack the concentration to fill in long and detailed questionnaires. The PHQ-4 consists of four items that can be answered on a four-point Likert-type scale (0 = not at all, 1 = several days, 2 = more than half days, 3 = nearly every day). Two items measure the core symptoms of depression and form the PHQ-2 subscale of the PHQ-4. Two items measure the core symptoms of anxiety and form the 3.2. Scales Manual 129 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Table 27 – continued from previous page year variable count mean sd itemrestcorr alpha 2006-2020 jl0367R 6958 4.70 1.64 0.25 0.47 2006-2020 jl0370 6970 5.58 1.28 0.25 0.47 2006-2020 jl0377 6971 5.82 1.10 0.41 0.47 2006-2020 jl0369 6970 4.49 1.73 0.36 0.57 2006-2020 jl0374 6970 3.96 1.69 0.45 0.57 2006-2020 jl0379R 6966 3.49 1.53 0.33 0.57 Big Five (13-14 year olds, self-report) Items and Scale Statistics year variable count mean sd itemrestcorr alpha 2016-2020 char14 2825 4.77 1.52 0.41 0.59 2016-2020 char19 2796 4.03 2.12 0.37 0.59 2016-2020 char24 2823 5.23 1.65 0.41 0.59 2016-2020 char26 2783 4.97 1.57 0.34 0.59 2016-2020 char11 2828 5.06 1.38 0.60 0.70 2016-2020 char17R 2827 4.15 1.89 0.43 0.70 2016-2020 char21 2827 4.99 1.37 0.57 0.70 2016-2020 char12 2829 5.28 1.52 0.52 0.64 2016-2020 char18 2809 4.96 1.57 0.50 0.64 2016-2020 char22R 2817 4.18 1.78 0.35 0.64 2016-2020 char13R 2824 5.16 1.56 0.30 0.52 2016-2020 char16 2830 5.49 1.46 0.29 0.52 2016-2020 char23 2829 5.81 1.21 0.44 0.52 2016-2020 char15 2827 3.57 1.88 0.36 0.55 2016-2020 char20 2828 3.47 1.74 0.45 0.55 2016-2020 char25R 2822 3.50 1.59 0.27 0.55 Big Five (11-12 year olds, self-report) Items and Scale Statistics year variable count mean sd itemrestcorr alpha 2014-2020 char14 4290 4.85 1.57 0.42 0.58 2014-2020 char19 4269 4.35 2.07 0.35 0.58 2014-2020 char24 4306 5.41 1.61 0.39 0.58 2014-2020 char26 4255 5.02 1.63 0.31 0.58 2014-2020 char11 4319 4.94 1.41 0.56 0.68 2014-2020 char17R 4320 4.54 1.85 0.42 0.68 2014-2020 char21 4293 4.91 1.46 0.54 0.68 2014-2020 char12 4310 5.38 1.51 0.46 0.57 2014-2020 char18 4257 5.05 1.54 0.43 0.57 2014-2020 char22R 4294 4.29 1.81 0.29 0.57 2014-2020 char13R 4301 5.25 1.58 0.31 0.53 2014-2020 char16 4316 5.64 1.44 0.29 0.53 continues on next page 136 Chapter 3. Survey Design SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Table 29 – continued from previous page year variable count mean sd itemrestcorr alpha 2014-2020 char23 4316 5.86 1.19 0.47 0.53 2014-2020 char15 4310 3.44 1.88 0.35 0.51 2014-2020 char20 4304 3.41 1.74 0.41 0.51 2014-2020 char25R 4286 3.74 1.61 0.23 0.51 Big Five (9-10 year olds & 5-6 year olds, parent reports) References Asendorpf, J. B., & van Aken, M. A. G. (2003). Validity of Big Five personality judgments in childhood: A 9 year longitudinal study. European Journal of Personality, 17, 1-17. Weinert, S., Asendorpf, J. B., Beelmann, A., Doil, H., Frevert, S., Lohaus, A., & Hasselhorn, M. (2007). Expertise zur Erfassung von psychologischen Personmerkmalen bei Kindern im Alter von fünf Jahren im Rahmen des SOEP. Berlin: DIW Berlin. Items How would you rank your child in comparison to other children of the same age (Wie würden Sie Ihr Kind im Vergleich zu anderen Kindern gleichen Alters beurteilen): Openness/Intellect 1. Child is not that interested – hungry for knowledge (ist wenig interessiert – ist wissensdurstig). 2. Child understands quickly – needs more time (begreift schnell – braucht mehr Zeit). Scale: 0 to 10 Conscientiousness 1. Child is tidy – untidy (ist unordentlich – ist ordentlich). 2. Child is focused – easy to distract (ist konzentriert – ist leicht ablenkbar). Scale: 0 to 10 Extraversion 1. Child is talkative – quiet (ist gesprächig – ist still). 2. Child is withdrawn – sociable (ist zurückgezogen– ist kontaktfreudig). Scale: 0 to 10 Agreeableness 1. Child is good-natured – irritable (ist gutmütig – ist reizbar). 2. Child is obstinate – compliant (ist trotzig – ist fügsam). Scale: 0 to 10 Neuroticism 1. Child is self-confident – insecure (hat Selbstvertrauen – ist unsicher). 2. Child is fearful – fearless (ist ängstlich – ist unängstlich). Scale: 0 to 10 Items and Scale Statistics 3.2. Scales Manual 137 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 year variable count mean sd 2012-2020 char7 7112 7.68 2.27 2012-2020 char4R 7112 6.95 2.86 2012-2020 char5 7122 5.03 2.90 2012-2020 char2R 7100 5.44 3.12 2012-2020 char1bR 7124 7.26 2.63 2012-2020 char9 7110 7.60 2.33 2012-2020 char6R 7092 7.00 2.67 2012-2020 char3 7097 5.84 2.57 2012-2020 char8 7109 3.46 2.71 2012-2020 char10R 7108 3.69 2.58 Items and Scale Statistics year variable count mean sd 2008-2020 char7 7385 8.27 1.99 2008-2020 char4R 7388 7.54 2.48 2008-2020 char5 7403 5.62 2.60 2008-2020 char2R 7386 6.07 2.82 2008-2020 char1bR 7398 7.63 2.49 2008-2020 char9 7379 7.88 2.19 2008-2020 char6R 7376 7.15 2.44 2008-2020 char3 7376 5.71 2.46 2008-2020 char8 7383 3.16 2.59 2008-2020 char10R 7388 3.61 2.51 Big Five (2-3 year olds, parent reports) References Asendorpf, J. B., & van Aken, M. A. G. (2003). Validity of Big Five personality judgments in childhood: A 9 year longitudinal study. European Journal of Personality, 17, 1-17. Weinert, S., Asendorpf, J. B., Beelmann, A., Doil, H., Frevert, S., Lohaus, A., & Hasselhorn, M. (2007). Expertise zur Erfassung von psychologischen Personmerkmalen bei Kindern im Alter von fünf Jahren im Rahmen des SOEP. Berlin: DIW Berlin. Items How would you rank your child in comparison to other children of the same age (Wie würden Sie Ihr Kind im Vergleich zu anderen Kindern gleichen Alters beurteilen): Openness/Intellect 1. Quick at learning new things – needs more time (begreift eher schnell – braucht mehr Zeit). Scale: 0 to 10 Conscientiousness 1. Focused – easily distracted (ist konzentriert – ist leicht ablenkbar). Scale: 0 to 10 Extraversion 138 Chapter 3. Survey Design SOEP Survey Paper 1261 SOEPcompanion, Release 2023 1. Shy – outgoing (ist eher schüchtern– ist eher kontaktfreudig). Scale: 0 to 10 Agreeableness 1. Obstinate – obedient (ist eher trotzig – ist eher fügsam/folgsam). Scale: 0 to 10 Items and Scale Statistics year variable count mean sd 2005-2020 char4 7018 2.25 2.31 2005-2020 char2 7006 4.15 2.66 2005-2020 char1a 7020 7.49 2.46 2005-2020 char3 7001 4.89 2.50 3.2.19 Reciprocity Summary Reciprocity is understood as the tendency to respond in kind to the actions of other people, that is, to respond to actions directed at oneself by acting in the same or a similar way (Fehr & Schmidt, 2005; Perugini et al., 2003). A distinction is drawn here between negative and positive reciprocity. Negative reciprocity is the tendency to respond to bad treatment by another person in a similarly negative way. Positive reciprocity relates to the tendency to respond positively to a positive experience. Reciprocity was measured in the SOEP in 2005, 2010, 2015, and 2020. Theoretical Background The scale is designed to measure reciprocity as an internalized norm within individuals. Reciprocity is understood as the tendency to respond to the actions of other people in kind, that is, to respond to actions directed at oneself by acting in the same or a similar way (Fehr & Schmidt, 2005; Perugini et al., 2003). The action may either have already occurred or be expected in the future. The concept of reciprocity can be used on three levels: 1. To describe real patterns of social exchange in society, 2. To describe the belief of social actors themselves that (and to what extent), from their point of view, relationships are regulated by the norm of reciprocity, 3. To describe the extent to which reciprocity is a norm that governs social actors in their daily interactions and internalized by individuals (Gouldner, 1960). Gouldner also argues that reciprocity is a basic norm that can be found in all historically known societies. Negative reciprocity can be distinguished from positive reciprocity. Negative reciprocity describes the tendency to respond negatively to negative treatment by another person and thus to negatively sanction this behavior. Positive reciprocity describes the tendency to approve and positively sanction positive interpersonal experiences, that is, to return favors. This distinction is required because different individuals respond more or less sensitively to positive or negative experiences and tend toward more or less positive or negative sanctions. Thus, individuals may be careful to always return favors but may at the same time (possible motivated by Christian norms of forgiveness) refrain from taking revenge or imposing penalties on others (Perugini et al. 2003). Empirically, positive and negative reciprocity are uncorrelated (Egloff, Richter & Schmukle, 2013). Both positive and negative reciprocity could be of interest in research on the motives for providing support of various kinds to friends and relatives or to engage in volunteer work, behavior in dilemma situations (game theory), and on forms of reciprocity as determinants of stability in social relationships. Scale Development The original scale (Perugini et al., 2003) consists of 27 items (nine per subscale) that were shortened for use in a survey to a total of nine (three per subscale), taking the three items with the highest factor loadings for each of the subscales. The three original dimensions could not, however, be extracted by means of principal component analysis in the 2004 SOEP pretest: the dimension “belief in reciprocity” is comprised of the dimensions “positive reciprocity” and “negative reciprocity.” That is, the theoretically postulated dimension “belief in reciprocity” is not empirically orthogonal to 3.2. Scales Manual 139 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 behavior. Thus, only the two factors “negative reciprocity” and “positive reciprocity” can be found consistently in the sample. Thus, in the shortened version of the questionnaire, only the six items in these two dimensions were used in the main SOEP survey. Further information on scale development and validity can be found in Dohmen et al. (2008, 2009). References Dohmen, T., Falk, A., Huffman, D., & Sunde, U. (2008). Representative trust and reciprocity: Prevalence and determinants. Economic Inquiry, 46, 81-90. Dohmen, T., Falk, A., Huffmann, D., & Sunde, U. (2009). Homo reciprocans: Survey evidence on behavioral outcomes. Economic Journal, 119, 592 - 612. Egloff, B., Richter, D., & Schmukle, S. C. (2013). Need for conclusive evidence that positive and negative reciprocity are unrelated. Comment on Yamagishi, T., et al. (2012) Rejection of unfair offers in the ultimatum game is no evidence of strong reciprocity. Proceedings of the National Academy of Sciences of the USA, 110, 786. Fehr, E. & Schmidt, K. M. (2006). The economics of fairness, reciprocity and altruism. Experimental evidence and new theories. Handbook of the Economics of Giving, Altruism and Reciprocity, 615-691. Gouldner, A. W. (1960). The Norm of Reciprocity: A Preliminary Statement. American Sociological Review, 25, 161-178. Perugini, M., Gallucci, M., Presaghi, F., & Ercolani, A. P. (2003). The personal norm of reciprocity. European Journal of Personality, 17, 251-283. Items To what degree do the following statements apply to you personally (In welchem Maße treffen die folgenden Aussagen auf Sie persönlich zu): Positive 1. If someone does me a favor, I am prepared to return it (Wenn mir jemand einen Gefallen tut, bin ich bereit, dies zu erwidern) 2. I go out of my way to help somebody who has been kind to me in the past (Ich stenge mich besonders an, um jemandem zu helfen, der mir früher schon mal geholfen hat) 3. I am ready to assume personal costs to help somebody who helped me in the past (Ich bin bereit, Kosten auf mich zu nehmen, um jemanden zu helfen, der mir früher geholfen hat ) Scale: 1 (Does not apply to me at all / Trifft überhaupt nicht zu) to 7 (Applies to me perfectly / Trifft voll zu) Test-Retest Correlations Positive reciprocity was included in a retest taken by subsamples (N = 158) in 2005 within 30 to 49 days after the initial test. Test-retest correlations of the items were (in scale order) .25, .35, and .36; test-retest correlation of scale scores was .42. Items and Scale Statistics year variable count mean sd itemrestcorr alpha 2005 plh0206i01 21015 6.45 0.88 0.41 0.64 2005 plh0206i04 20975 5.90 1.17 0.55 0.64 2005 plh0206i06 20949 5.28 1.48 0.45 0.64 2010 plh0206i01 18857 6.42 0.90 0.38 0.61 2010 plh0206i04 18822 5.88 1.18 0.50 0.61 2010 plh0206i06 18800 5.22 1.49 0.43 0.61 2015 plh0206i01 26987 6.40 0.94 0.35 0.61 2015 plh0206i04 26967 5.90 1.18 0.52 0.61 continues on next page 140 Chapter 3. Survey Design SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Table 33 – continued from previous page year variable count mean sd itemrestcorr alpha 2015 plh0206i06 26877 5.31 1.49 0.43 0.61 2016 plh0206i01 4400 6.79 0.77 0.31 0.55 2016 plh0206i04 4393 6.78 0.71 0.46 0.55 2016 plh0206i06 4345 6.45 1.12 0.37 0.55 2017 plh0206i01 2883 6.82 0.62 0.31 0.56 2017 plh0206i04 2874 6.75 0.71 0.45 0.56 2017 plh0206i06 2854 6.42 1.16 0.45 0.56 2018 plh0206i01 426 6.81 0.62 0.28 0.53 2018 plh0206i04 422 6.72 0.84 0.44 0.53 2018 plh0206i06 422 6.34 1.29 0.40 0.53 2019 plh0206i01 280 6.71 0.76 0.52 0.71 2019 plh0206i04 277 6.48 1.16 0.61 0.71 2019 plh0206i06 279 6.14 1.38 0.54 0.71 2020 plh0206i01 30344 6.42 0.96 0.38 0.65 2020 plh0206i04 30309 5.97 1.19 0.55 0.65 2020 plh0206i06 30253 5.57 1.40 0.48 0.65 Items To what degree do the following statements apply to you personally (In welchem Maße treffen die folgenden Aussagen auf Sie persönlich zu): Negative 1. If I suffer a serious wrong, I will take revenge as soon as possible, no matter what the cost (Wenn mir schweres Unrecht zuteilwird, werde ich mich um jeden Preis bei der nächsten Gelegenheit dafür rächen) 2. If somebody puts me in a difficult position, I will do the same to him/her (Wenn mich jemand in eine schwierige Lage bringt, werde ich das Gleiche mit ihm zu machen) 3. If somebody offends me, I will offend him/her back (Wenn mich jemand beleidigt, werde ich mich ihm gegenüber beleidigend verhalten) Scale: 1 (Does not apply to me at all / Trifft überhaupt nicht zu) to 7 (Applies to me perfectly / Trifft voll zu) Test-Retest Correlations Negative reciprocity was included in a retest taken by a subsample (N = 158) in 2005 within 30 to 49 days after the initial test. Test-retest correlations of the items were (in scale order) .44, .42, and .58; test-retest correlation of scale scores was .64. Items and Scale Statistics year variable count mean sd itemrestcorr alpha 2005 plh0206i02 20947 3.21 1.73 0.71 0.83 2005 plh0206i03 20925 2.88 1.63 0.73 0.83 2005 plh0206i05 20952 3.24 1.73 0.61 0.83 2010 plh0206i02 18822 3.18 1.69 0.70 0.82 2010 plh0206i03 18785 2.83 1.59 0.73 0.82 2010 plh0206i05 18805 3.12 1.70 0.59 0.82 2015 plh0206i02 26921 2.77 1.67 0.68 0.81 2015 plh0206i03 26936 2.47 1.53 0.73 0.81 2015 plh0206i05 26939 2.85 1.70 0.56 0.81 continues on next page 3.2. Scales Manual 141 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Table 34 – continued from previous page year variable count mean sd itemrestcorr alpha 2016 plh0206i02 4298 1.86 1.67 0.58 0.75 2016 plh0206i03 4324 1.58 1.35 0.64 0.75 2016 plh0206i05 4341 1.89 1.65 0.55 0.75 2017 plh0206i02 2815 1.68 1.47 0.56 0.72 2017 plh0206i03 2842 1.62 1.43 0.57 0.72 2017 plh0206i05 2857 1.85 1.65 0.50 0.72 2018 plh0206i02 413 1.90 1.67 0.52 0.68 2018 plh0206i03 414 1.76 1.62 0.57 0.68 2018 plh0206i05 421 2.32 2.00 0.42 0.68 2019 plh0206i02 271 2.12 1.68 0.58 0.75 2019 plh0206i03 276 2.02 1.60 0.65 0.75 2019 plh0206i05 276 2.43 1.88 0.53 0.75 2020 plh0206i02 30253 2.57 1.60 0.67 0.79 2020 plh0206i03 30278 2.30 1.47 0.70 0.79 2020 plh0206i05 30285 2.72 1.67 0.55 0.79 3.2.20 Risk Aversion Summary The global and dimension-specific measurement of risk aversion consists of seven items and was used in the SOEP in 2004 and 2009. An individual item on general risk aversion has been included in the SOEP at two-year intervals since 2004 and annually since 2008. Theoretical Background Risks and uncertainties play a role in almost all important life decisions. In their pioneering work on the subject, Kahneman and Tversky (1979) developed a theoretical foundation for the analysis of risk attitudes in the form of “Prospect Theory.” This theory describes the process of decision making in situations of uncertainty, and postulates that the consequences of a decision are evaluated relative to a reference point as either gains or losses, also considering the probability of their occurrence. Here, individual risk attitudes affect not only how individuals evaluate the potential gains and losses but also how they evaluate the respective probabilities of the event’s occurrence. In the economic research, individual risk attitudes are used to explain decisions about financial investments, labor market behavior and success, and health behavior (e.g., Dohmen et al., 2011). More recent work in the field of educational sociology has studied the influence of individual risk aversion on educational decisions (Hartlaub & Schneider, 2013). Scale Development The scale was first piloted in the 2003 SOEP pretest and was then used in the 2004 main SOEP survey. Simultaneously to the SOEP pretest, a behaviorally oriented experiment on risk attitudes was carried out with respondents (see Dohmen et al., 2011). In this experiment, respondents were shown a table consisting of 20 text lines and were given the task of deciding in each line whether they would prefer to receive a fixed amount of money or take part in a lottery with a 50% chance of winning 300 euros. In the case of the “safe” option, the amount paid increased progressively from one line to the next (0 euros in the first, 10 euros in the second, and so on up to 190 euros in line 20). Since the expected lottery winnings would be 150 euros, more risk-averse respondents should take the safe option below this value, while riskseeking participants can be expected to decide for the lottery even with safe options of 160, 170, 180, or 190 euros. The amount paid for the safe option at the time of switching to this option was regressed on the value of the questionnaire on risk aversion. Here, the behavior in the experiment could be predicted well based on the survey responses (ß= .61, p < .001). References 142 Chapter 3. Survey Design SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Dohmen, T., Falk, A., Huffman, D., Sunde, U., Schupp, J., & Wagner, G. G. (2011). Individual Risk Attitudes: Measurement, Determinants and Behavioral Consequences. Journal of the European Economic Association, 9, 522-550. Hartlaub, V. & Schneider, T. (2013). Educational choice and risk preferences: How important is relative vs. individual risk preference? Manuscript submitted for publication.). Kahneman, D. & Tversky, A. (1979). Prospect Theory: An analysis of decision under risk. Econometrica: Journal of the Econometric Society, 47, 263-291. Items How do you see yourself (Wie schätzen Sie sich persönlich ein): Risk Aversion in General 1. Are you generally a person who is fully prepared to take risks or do you try to avoid taking risks? (Sind Sie im Allgemeinen ein risikobereiter Mensch oder versuchen Sie, Risiken zu vermeiden?) Scale: 0 (Risk averse / Gar nicht risikobereit) to 10 (Fully prepared to take risks / Sehr risikobereit) Test-Retest Correlations Risk aversion in general was included in retests taken by subsamples in 2005, 2006, and 2009 within 30 to 49 days after the respective initial tests. Test-retest correlation pooled across all three waves (N = 607) was .60. Items and Scale Statistics year variable count mean sd 2004 plh0204_v2 21881 4.42 2.38 2006 plh0204_v2 22210 4.77 2.29 2008 plh0204_v2 19639 4.46 2.31 2009 plh0204_v2 20707 3.74 2.21 2010 plh0204_v2 26628 4.42 2.35 2011 plh0204_v2 21011 4.54 2.27 2012 plh0204_v2 27903 4.86 2.26 2013 plh0204_v2 19117 4.51 2.40 2014 plh0204_v2 27275 4.77 2.42 2015 plh0204_v2 27116 4.87 2.44 2016 plh0204_v2 28765 4.86 2.61 2017 plh0204_v2 32247 4.66 2.60 2018 plh0204_v2 30132 4.29 2.62 2019 plh0204_v2 29792 4.99 2.57 2020 plh0204_v2 30334 4.84 2.61 year variable count mean sd 2006-2020 jl0349 6994 5.88 2.24 year variable count mean sd 2016-2020 char30 2855 4.89 2.48 3.2. Scales Manual 143 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 year variable count mean sd 2014-2020 char30 4340 5.09 2.57 Items How would you rate your willingness to take risks in the following areas? How is it ... (Wie würden Sie Ihre Risikobereitschaft in Bezug auf die folgenden Bereiche einschätzen? Wie ist das ...): Risk Aversion in different Domains 1. While driving (beim Autofahren)? 2. In financial matters (bei Geldanlagen)? 3. During leisure and sport (bei Freizeit und Sport)? 4. In your occupation (bei Ihrer beruflichen Karriere)? 5. With your health (bei Ihrer Gesundheit)? 6. Your faith in other people (bei Vertrauen in fremde Menschen)? Scale: 0 (Risk averse / Gar nicht risikobereit) to 10 (Fully prepared to take risks / Sehr risikobereit) Test-Retest Correlations Risk aversion in different domains was included in a retest of a subsample (N = 120) in 2009 within 30 to 49 days after the respective initial tests. Test-retest correlation of the items were (in scale order) .67, .48, .58, .69, .51, and .37; scale scores had a test-retest correlation of .69. Items and Scale Statistics year variable count mean sd itemrestcorr alpha 2004 plh0197 20604 2.93 2.53 0.63 0.85 2004 plh0198 21691 2.41 2.23 0.62 0.85 2004 plh0199 21574 3.49 2.61 0.68 0.85 2004 plh0200 19902 3.60 2.71 0.68 0.85 2004 plh0201 21868 2.93 2.47 0.65 0.85 2004 plh0202 21890 3.35 2.40 0.50 0.85 2009 plh0197 19426 2.98 2.57 0.62 0.82 2009 plh0198 20472 1.90 2.14 0.56 0.82 2009 plh0199 20274 3.20 2.63 0.67 0.82 2009 plh0200 18077 3.21 2.72 0.64 0.82 2009 plh0201 20696 2.71 2.44 0.63 0.82 2009 plh0202 20712 3.23 2.39 0.43 0.82 2014 plh0197 25254 3.51 2.63 0.60 0.82 2014 plh0198 26442 2.37 2.25 0.59 0.82 2014 plh0199 26791 3.82 2.60 0.65 0.82 2014 plh0200 23532 3.87 2.65 0.64 0.82 2014 plh0201 27254 3.21 2.46 0.63 0.82 2014 plh0202 27290 3.57 2.41 0.44 0.82 144 Chapter 3. Survey Design SOEP Survey Paper 1261 SOEPcompanion, Release 2023 3.2.21 Self Esteem Summary Global self-esteem—a person’s overall evaluation or appraisal of his or her worth— was measured in the SOEP in 2010, 2015, and 2020 with an individual item. Theoretical Background Self-esteem is a central construct in psychology and has value both as a predictor variable (e.g., for the occurrence of various life events) and as a target variable with great potential significance for numerous research questions. Scale Development In the SOEP, following on the paper by Robins, Hendin, & Trzesniewski (2001), an individual item was used to measure self-esteem. References Robins, R. W., Hendin, H. M., & Trzesniewski, K. H. (2001). Measuring global self-esteem: Construct validation of a single-item measure and the Rosenberg Self-Esteem Scale. Personality and Social Psychology Bulletin, 27, 151-161. Items To what degree do the following statements apply to you personally (In welchem Maße treffen die folgenden Aussagen auf Sie persönlich zu): 1. I have a positive attitude toward myself (Ich habe eine positive Einstellung zu mir selbst). Scale: 1 (Does not apply to me at all / Trifft überhaupt nicht zu) to 7 (Applies to me perfectly / Trifft voll zu) Items and Scale Statistics year variable count mean sd 2010 plh0206i11 18817 5.58 1.28 2015 plh0206i11 26964 5.65 1.29 2016 plh0206i11 4195 6.27 1.18 2017 plh0206i11 2743 6.34 1.17 2018 plh0206i11 418 6.39 1.11 2019 plh0206i11 279 6.35 1.02 2020 plh0206i11 30301 5.74 1.29 year variable count mean sd 2010-2020 jl1380 5822 5.28 1.44 year variable count mean sd 2016 char27 2814 5.21 1.50 3.2. Scales Manual 145 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 (continued from previous page) 9tabstat diff2, by(quit) To obtain a weighted mean value, address the analysis weight after the generated variable. 1tabstat diff2 [aw=phrf], by(quit) /*weighted*/ This illustration shows the mean of the health variable under the condition of the quit variable that we generated beforehand. With a mean of -0.24 (weighted -0.35), the biggest change in health satisfaction is seen in people who quit smoking after 2006. For example, if a person smoked in 2006 and indicated a satisfaction value of 8, the person indicates a satisfaction value of 7.76 after he/she stopped smoking in 2008. So you can assume that when a person stops smoking, their perceived health state deteriorates. Now we have to test if the assumption is correct. d. Does quitting smoking make your health worse? To what extent could the result of the analysis “stop smoking” be distorted? In order to establish a connection between health satisfaction and stopping smoking, one should use the t-test or to be more specific, the one-sample t-test. It checks whether the mean value of a sample deviates significantly from a known expected value (specified in the null hypothesis). 248 Chapter 6. Working with SOEP Data SOEP Survey Paper 1261 SOEPcompanion, Release 2023 1*Notes: So far we have not tested whether the difference is␣ ˓→statistically significant 2ttest diff2==0if quit==1 H0 Hypothesis: If one stops smoking, it has no effect on health. For this test we assume a 95% probability. What we want to check now is whether the H0 hypothesis can be rejected or not. If you look at the output of the test, you first see the mean value of 1 (quit smoking) of the variable quit. The last line of the output shows the significance level. If it falls below the value 0.05, one can speak of a statistically significant result. In our example, the null hypothesis can be discarded because its value is less than 0.05 percent. So quitting smoking has a significant impact on a person’s perceived health. Last change: May 30, 2023 6.5 Working with harmonized Variables This exercise shows you how to work effectively with versioned and harmonized SOEP variables. Please note that the new SOEP versioning and harmonizing concept has only been available since SOEP-Core v34 and only applies to the original SOEP-Core data in long format. Create an exercise path with four subfolders: Example: •H:/material/exercises/do •H:/material/exercises/output •H:/material/exercises/temp 6.5. Working with harmonized Variables 249 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 •H:/material/exercises/log These are used to store your script, log files, datasets, and temporary datasets. Open an empty do-file and define your paths with globals: 1*********************************************** 2*Set relative paths to the working directory 3*********************************************** 4global AVZ "H:\material\exercises" 5global MY_IN_PATH "\\hume\rdc-gen\consolidated\soep-long\soep.v34" 6global MY_DO_FILES "$AVZ\do\" 7global MY_LOG_OUT "$AVZ\log\" 8global MY_OUT_DATA "$AVZ\output\" 9global MY_OUT_TEMP "$AVZ\temp\" The global “AVZ” defines the main path. The main paths are subdivided using the globals “MY_IN_PATH”, “MY_DO_FILES”, “MY_LOG_OUT”, “MY_OUT_DATA”, “MY_OUT_TEMP”. The global “MY_IN_PATH” contains the path to your ordered data. 1.) Differences in Response Options Variables are versioned and harmonized because the response options have changed over time. The variable plb0038_v1 was obtained from a simple yes/no question between 1992 and 2004. Since 2005, new response options have been added. The individual questionnaires from 2004 and 2005 show these differences. Through the versioning of the variable plb0038, this difference is recognizable to the data user when tabulating the variable. The variable label also shows the beginning and end of the period in which the question was asked differently. 1use "$MY_IN_PATH\pl.dta" 2tab plb0038_v1 3tab plb0038_v2 250 Chapter 6. Working with SOEP Data SOEP Survey Paper 1261 SOEPcompanion, Release 2023 The variable plb0038_v1 is recoded during the harmonization process and written into a new variable, plb0038_h, together with plb0038_v2. The harmonized version of the variable should cover the survey period from 1992 to 2014 and should be usable. 1tab plb0038_h 2tabstat plb0038_v1 plb0038_v2 plb0038_h, by(syear) 6.5. Working with harmonized Variables 251 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 2.) Differences in Coding of Response Options Variables are versioned and harmonized because the coding of the response options has changed over time. Since the 252 Chapter 6. Working with SOEP Data SOEP Survey Paper 1261 SOEPcompanion, Release 2023 values of certain response options can change, the various wave-specific variables cannot be integrated easily into a variable in long format. The variable must be appropriately harmonized to be useable. From 1994 to 2004, the question about “job change” was asked in the individual questionnaire as a category question with six response options. The order of the response options changed in 2005. 6.5. Working with harmonized Variables 253 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 1tab plb0284_v1 2tab plb0284_v2 In addition to the different order of the response options, the coding order also changed. The data are stored in the wave-specific “raw” datasets with different coding and are contained in the variables plb0284_v1 and plb0284_v2. To use the variable for all survey years, it is necessary to harmonize the different versions. The variable plb0284_v1 is recoded (recode (1=1)(2=2)(3=3)(4=6)(5=4)(6=5)) and then written together with plb0284_v2 as plb0284_h. The new variable plb0284_h is created by the harmonization process. 1tab plb0284_h 2tabstat plb0284_v1 plb0284_v2 plb0284_h, by(syear) 254 Chapter 6. Working with SOEP Data SOEP Survey Paper 1261 SOEPcompanion, Release 2023 6.5. Working with harmonized Variables 255 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 3.) Content Differences in the Questions. Variables are versioned when questions were asked differently in different years but the content belongs together. If the 256 Chapter 6. Working with SOEP Data SOEP Survey Paper 1261 SOEPcompanion, Release 2023 content or wording of the question changes, the wave-specific variables cannot easily be integrated into a long variable. In the 2001 individual questionnaire, respondents were asked whether they had ever received an inheritance. In 2017, this question was worded differently: respondents were asked whether they had received an inheritance in the last 15 years. The questions are similar but cover different time periods. Therefore, the variable is not harmonized but made available as versioned variables. Data users have to decide whether or not to use the variables in the same way. 1tab plc0375_v1 2tab plc0375_v2 6.5. Working with harmonized Variables 257 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 6.6 Longitudinal Data Analysis Simple cross-sectional analyses show that married people have higher life satisfaction than singles. You want to check this on the basis of longitudinal analysis with the SOEP. Create an exercise path with four subfolders: Example: •H:/material/exercises/do •H:/material/exercises/output •H:/material/exercises/temp •H:/material/exercises/log These are used to store your script, log files, datasets, and temporary datasets. Open an empty do-file and define the paths you created with globals: 1*********************************************** 2*Set relative paths to the working directory 3*********************************************** 4global AVZ "H:\Exercise\" 5global MY_IN_PATH "\\hume\rdc-prod\distribution\soep-core\soep.v37\eu\ ˓→Stata\" 6global MY_DO_FILES "$AVZ\do\" 7global MY_LOG_OUT "$AVZ\log\" 8global MY_OUT_DATA "$AVZ\output\" 9global MY_OUT_TEMP "$AVZ\temp\" The global “AVZ” defines the main path. The main paths are subdivided using the globals “MY_IN_PATH”, “MY_DO_FILES”, “MY_LOG_OUT”, “MY_OUT_DATA”, “MY_OUT_TEMP”. The global “MY_IN_PATH” contains the path to your ordered data. Create a master file that uses the important variables from ppathl. You should always add some variables from PPATHL to your dataset by default. Download the following information from PPATHL: •Individual identifier "pid" •Household identifier "pid" •Survey year "syear" •The net variable with information on the interview type "netto" •The weighting variable "phrf" •The gender of the person "sex" •The migration background "migback" 264 Chapter 6. Working with SOEP Data SOEP Survey Paper 1261 SOEPcompanion, Release 2023 1*** Step 1) Start with basic information from PPFADL *** 2 3use pid hid syear netto phrf migback sex using ${MY_IN_PATH}\ppathl.dta Search for matching variables and add them to your dataset To perform your analysis, you need different SOEP variables. The SOEP offers various options for a variable search: •Search the questionnaires for useful variables. (for more information, see the section Variable Search with Questionnaires) •Find a suitable variable via the topic list of paneldata.org (for more information, see the section Topic Search with paneldata.org) •Search for a suitable variable using a search term in paneldata.org (for more information, see the section Variable Search with paneldata.org) •Use the documentation provided on the generated variables (for more information, see the section Documentation on Generated Data) In this case, we use the variables "pgfamstd" (martial status) and "plh0182" (life satisfaction). 1*** Step 2) Add the relavant variables: here: family status and life␣ ˓→satisfaction *** 2merge 1:1 pid syear using ${MY_IN_PATH}\pgen, keepusing(pgfamstd) keep(1␣ ˓→3) nogen 3 4merge 1:1 pid syear using ${MY_IN_PATH}\pl, keepusing(plh0182) keep(1 3)␣ ˓→nogen 5 6save $MY_OUT_DATA\ppathl.dta, replace 6.6.1 Clean and inspect the data Encode all missing values to system missing. Since you are interested in individual characteristics in your analysis: Delete all measurements that are not based on successful individual interviews. 1mvdecode _all, mv(-8/-1) 2 3tab netto 4drop if netto>19 6.6. Longitudinal Data Analysis 265 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 How many people contribute measurements and what is the proportion of people contributing at least 10 waves in a row? Define the dataset as a panel dataset. 1xtset pid syear (continues on next page) 266 Chapter 6. Working with SOEP Data SOEP Survey Paper 1261 SOEPcompanion, Release 2023 (continued from previous page) 2xtdes 105,068 respondents have contributed information in waves a (1984) to bk (2020) and 75% of the 105,068 respondents have provided information for at least 10 waves. How many people took part in the survey in 2010 and contributed to continuous measurements up to 2014? 1xtdes if syear>=2010 &syear<=2014 6.6. Longitudinal Data Analysis 267 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 14,673 respondents provided continuous information from 2010 to 2014. 6.6.2 Univariate inspection & analysis How does the mean of life satisfaction change over time? 1*** Step 4) univariate inspection &analysis 2table syear, statistic (mean plh0182) 268 Chapter 6. Working with SOEP Data SOEP Survey Paper 1261 SOEPcompanion, Release 2023 What proportion of people are a) married in 2014 or b) have a migration background? Compare weighted with unweighted frequency tables: Who is overrepresented in SOEP? 6.6. Longitudinal Data Analysis 269 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 1tab1 pgfamstd migback if syear==2014 2tab pgfamstd [aw=phrf] if syear==2014 3tab migback [aw=phrf] if syear==2014 The data show that married people are overrepresented in the SOEP and single people are underrepresented. The weighting makes it representative again for Germany. 270 Chapter 6. Working with SOEP Data SOEP Survey Paper 1261 SOEPcompanion, Release 2023 In the SOEP sample, respondents with a direct or indirect migration background are overrepresented. How many of those persons who reported a life satisfaction scale value of 7 in one survey year also indicated the scale value of 7 in the following survey year? 1xttrans plh0182 34.57% of the respondents who reported a life satisfaction of 7 again reported a value of 7 in the following year. Is it more likely that a highly dissatisfied person (value: 0) will be less dissatisfied the following year or that a very satisfied (value: 10) person will be less satisfied the following year? 6.6. Longitudinal Data Analysis 271 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 1xttrans plh0182 The rows reflect the initial values, and the columns reflect the final values. Around 20% of those who were completely dissatisfied (value: 0) in the base year remained completely dissatisfied in the following year. About 80% of these completely dissatisfied people from the base year were more satisfied in the following year. Of the completely satisfied persons (value: 10), about 37% remained just as satisfied in the following year, but 63% became less satisfied. It is more likely that a completely dissatisfied person will become more satisfied in the following year than that a completely satisfied person will become less satisfied. Which transitions in marital status can be observed particularly frequently in the data? 1xttrans pgfamstd Survey respondents who were married but lived separated [value 2] in the base year and reported divorce as their family status in the following year [value 4] can be observed particularly frequently (about 19%). 272 Chapter 6. Working with SOEP Data SOEP Survey Paper 1261 SOEPcompanion, Release 2023 6.6.3 Simple cross sectional analyses You now want to find the correlation between marital status and life satisfaction. Is there an effect of marriage on life satisfaction? And if so, is it a sustained effect? First, calculate the correlation between family status and life satisfaction from a cross-sectional perspective for 2010: Are married people happier than singles? 1table pgfamstd if syear==2010, statistic (mean plh0182) At first glance, married couples seem happier than singles. Now generate a variable that indicates a transition from “single” to “married”. How many such transitions can you find in the data? 1**define event: transition to marriage 2generate to_mar=1if pgfamstd==1&l.pgfamstd==3 3tab to_mar A total of 5,559 people can be observed changing status from single to married. What is the average level of life satisfaction immediately after the transition to marriage (i.e., in the first survey in which the transition can be observed) and how high is life satisfaction immediately before the transition to marriage? 1**standard way of life-event analysis 2sum plh0182 if to_mar==1 3sum l.plh0182 if to_mar==1 (continues on next page) 6.6. Longitudinal Data Analysis 273 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 1/* 2b) Merge the previously generated data set using the person␣ ˓→number. 3*/ 4 5merge 1:1 persnr using $MY_OUT_TEMP\biimgrp.dta, nogen c) Add the corresponding individual extrapolation factors to the data. 1c) Add the corresponding data using the individual identifier. 2*/ 3 4merge 1:1 persnr using $MY_IN_PATH\phrf.dta, keepus(bgphrf) nogen d) Only keep respondents who completed a youth or individual questionnaire in 2016. For example, to exclude children who have not provided immigration status information, use the net code from PPATH. Only keep individuals who completed an individual or youth interview. 1/* 2d) Only keep respondents who completed a youth or individual␣ ˓→questionnaire in 2016. 3*/ 4 5tab bgnetto, m //Variable values are displayed 6 7keep if inrange(bgnetto, 10,19)// People who have a code between 10␣ ˓→and 19 will be kept. 280 Chapter 6. Working with SOEP Data SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Task 3: Generate a status variable with the following categories:. •No migration background •Migrant, 2nd generation •Migrant, no information •Migrant, not refugee •Migrant, refugee To generate this status variable, check the contents of the existing migration variables from PPATH (migback germborn). 1/* 2Generate a status variable with the following categories: 3*/ 4 5tab migback 6.7. Working with Migration Data (BIOIMMIG) 281 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 1tab germborn Use the migration variables from PPATH (migback, germborn) and link this information with your previously generated refugee variable to build the described status variable from Task 3. 1gen Status =0// All persons will first receive the missing code for ˓→"no info". 2replace Status =1if migback == 1&germborn == 1// "no migback" 3replace Status =2if migback == 3// "2nd generation"␣ ˓→(2nd generation migrants born by definition in Germany, therefore "& germborn == 1"␣ ˓→here unnecessary 4replace Status =3if germborn == 2&Escape == 0// "Immigrants␣ ˓→without information" 5replace Status =4if germborn == 2&Escape == 1// "Immigrants, no␣ ˓→escape" 6replace Status =5if germborn == 2&Escape == 2// "Immigrant, escape" 7 8label def Statuslbl 0"no info" 1"no migback" 2"2. Generation" 3 ˓→"Immigrants without information" 4"Immigrants, no escape" 5"Immigrant, escape" 9label val Status Statuslbl // Values of the status veriable receive label Task 4: Content analysis: a) How many refugees (foreign-born with refugee/asylum status) are now in your file? Look at your status variable previously generated in task 3 to answer the question. 1*** Exercise 4␣ ˓→****************************************************************** 2 3/* 4a) How many refugees (foreign-born with refugee/asylum status)␣ (continues on next page) 282 Chapter 6. Working with SOEP Data SOEP Survey Paper 1261 SOEPcompanion, Release 2023 (continued from previous page) ˓→are now in your file? 5*/ 6 7tab Status, m //Display Generated Status Variable All 4,514 respondents who received the value 5 for the generated status variable have a direct migration background (migback==2), were not born in Germany (germborn==2), and fled their country of origin (flight==2 and biimgrp==5). b) How many are there if you take the individual extrapolation factors into account? Interpret the results. Look at the status variable generated in task 3 to answer the question. 1/* 2b) How many are there if you take the individual extrapolation␣ ˓→factors into account? Interpret the results. 3*/ 4 5tab Status [aw=bgphrf], m //Display generated status variable weighted␣ ˓→with analytic weights After weighting, there are approximately 675 refugees in the dataset. The weighting thus corrected the number of refugees downwards. c) How many persons are represented in the sample, taking the extrapolation factors into account? 6.7. Working with Migration Data (BIOIMMIG) 283 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 To use frequency weights in STATA, integer weights are required. Create an integer frequency weight from the weighting factor provided so that you can make representative statements. Then take a look at the new results. 1/* 2c) How many persons are represented when the sample taking the␣ ˓→extrapolation factors into account? 3*/ 4 5gen fweight = round(bgphrf) //Frequency weights for stata require␣ ˓→integer weight 6tab Status [fw=fweight], m //Display generated status variable weighted␣ ˓→with frequency weights Around 1,600,000 people are represented. d) What is the proportion of people over 40 years of age among the refugees? Since the data in this exercise come from the wave “bg”, we are currently in the survey year 2016; if you need a description of the wave designations, please refer to the chapter Label. To generate a suitable age variable, you can use the year of birth (year of birth). If we look at the survey year 2016, all persons born in 1976 or earlier were over 40 years old. Generate a suitable age variable and look at the proportion of refugees over 40 years of age in weighted form: 1/* 2d) What is the proportion of people over 40 years of age among␣ ˓→the refugees? 3*/ 4 5gen ue_40 = 0 6replace ue_40 = 1 if gebjahr <= 1976 // Persons receive proficiency 1 if␣ ˓→they were born before 1975. 7 8tab Status ue_40 [aw=bgphrf], m row nofreq 284 Chapter 6. Working with SOEP Data SOEP Survey Paper 1261 SOEPcompanion, Release 2023 The proportion of refugees over 40 years of age is about 47%. Last change: Jun 30, 2023 6.8 Fixed Effects Estimation Let’s say you want to find out whether certain variables relevant to the labor market, such as work experience or time in education, influence a person’s hourly wage. Other variables such as gender or marital status should also be taken into account. You decide to use the SOEP data to set up a fixed effects estimation model. Create a path with four subfolders: Example: •H:/material/exercises/do •H:/material/exercises/output •H:/material/exercises/temp •H:/material/exercises/log These are used to store your script, log files, datasets, and temporary datasets. Open an empty do-file and define your paths with globals: 1*********************************************** 2*Set relative paths to the working directory 3*********************************************** 4global AVZ "H:\material\exercises" 5global MY_IN_PATH "\\hume\rdc-prod\distribution\soep-long\soep.v33.1\ (continues on next page) 6.8. Fixed Effects Estimation 285 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 (continued from previous page) ˓→stata_en\" 6global MY_DO_FILES "$AVZ\do\" 7global MY_LOG_OUT "$AVZ\log\" 8global MY_OUT_DATA "$AVZ\output\" 9global MY_OUT_TEMP "$AVZ\temp\" The global “AVZ” defines the main path. The main paths are subdivided using the globals “MY_IN_PATH”, “MY_DO_FILES”, “MY_LOG_OUT”, “MY_OUT_DATA”, “MY_OUT_TEMP”. The global “MY_IN_PATH” contains the path to your data. a) Generate your own SOEPwage.dta dataset. The dataset should contain information on gross monthly wage, marital status, and other personal characteristics. To perform your analysis, you need different SOEP variables. The SOEP offers various options for a variable search: •Search the questionnaires for useful variables. (For more information, see the section Variable Search with Questionnaires) •Find a suitable variable in the topic list at paneldata.org (for more information, see the section Topic Search with paneldata.org) •Search for a suitable variable using a search term in paneldata.org (for more information, see the section Variable Search with paneldata.org) •Use the documentation provided for the generated variables (for more information, seethe section Documentation on Generated Data) Use the various important variables of the ppfadl.dta dataset as your start file. Your source file should contain the following variables: •Individual identifier "pid" •Survey year "syear" •Birth Year "gebjahr" •The net variable with information on the interview type "netto" •The weighting variable "phrf" •The gender of the person "sex" •Sample membership "pop" 1use pid syear sex gebjahr netto pop phrf using "${MY_IN_PATH}/ppfadl.dta ˓→", clear Attention: Please note that since version 34 (v34), PPFADL has been renamed PPATHL. The following ecxercises are done with version 33.1 (v33.1), where the tracking file was named PPFADL. Apply the necessary content variables to your starting dataset. You need the following variables for your analysis: •Employment status plb0022_h •Current gross income in euros "pglabgro" •Actual weekly working hours "pgtatzeit" •Full-time work experience "pgexpft" 286 Chapter 6. Working with SOEP Data SOEP Survey Paper 1261 SOEPcompanion, Release 2023 •Years of education or training "pgbilzeit" •Marital status in survey year "pgfamstd" 1merge 1:1pid syear using "${MY_IN_PATH}/pl.dta", keepus(plb0022)␣ ˓→keep(master match) nogen 2merge 1:1pid syear using "${MY_IN_PATH}/pgen.dta", keepus(pglabgro␣ ˓→pgtatzeit pgexpft pgbilzeit pgfamstd) keep(master match) nogen Only keep people who have completed an interview and who live in a private household. 1*Only select people with completed interviews 2keep if inrange(netto, 10,19) 3 4*Only private households 5keep if pop==1|pop==2 Since you are only interested in the period from 2012 to 2016, remove all survey information that does not fall within this period. To finish, save your dataset. 1*Period from 2012 to 2016 2keep if syear>=2012 &syear<=2016 Exercise 1: Prepare your dataset a) Load your created SOEPWage.dta dataset. It contains information on gross monthly wage, marital status, and other personal characteristics. 1*** Exercise 1: Prepare your dataset 2*a) Load data set 3use "${MY_OUT_DATA}/SOEPWage.dta", clear b) Recode all missing values in systemmissings (.) 1*b) Recode Missings 2mvdecode _all, mv(-8/-1= .) For more information about the missing codes for SOEP data, see the chapter Missing Conventions c) Generate the variables “hourly wage” (gross monthly wage/4.33*working time) for persons who have earned at least 1 euro and have worked at least one hour, “Married vs. Unmarried” and age. 1*c) Generate Variables 2gen wage =pglabgro/(4.33*pgtatzeit) if pglabgro>=1&pgtatzeit>=1 3 4gen married =1if pgfamstd==1|pgfamstd==6|pgfamstd==7|pgfamstd==8 5replace married =0if inrange(pgfamstd, 2,5) 6 7gen age =syear -gebjahr d) Adjust the variable “hourly wage” from outlier values by setting values smaller than the first percentile to the same value. Set values greater than 3 times the 99th percentile to 3*99th percentile. Then generate the variable lwage = log(wage). 6.8. Fixed Effects Estimation 287 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 1*d) Adjust wage variable 2sum wage, detail 3replace wage =1/3*r(p1) if wage<1/3*r(p1) 4replace wage =3*r(p99) if wage>3*r(p99) &wage<. 5 6gen lwage =log(wage) 7label variable lwage "Log hourly wage" 8 9save "${MY_OUT_DATA}/SOEPWage_temp.dta", replace Exercise 2: Descriptive statistics a) Define the dataset as a panel dataset. 1*** Exercise 2: Descriptive statistics 2*a) 3xtset pid syear // Declaring data as panel data b) What percentage of people participated in all five waves (xtdescribe) 1*b) 2xtdescribe, patterns(16)// -> unbalanced panel 288 Chapter 6. Working with SOEP Data SOEP Survey Paper 1261 SOEPcompanion, Release 2023 42808 respondents have contributed information within waves bc (2012) - bg (2016) and about 40% (17069) of the 42808 respondents have provided information for all waves. c) Describe the variable “Married” with xttab and xttrans. Take a look at some individual wage (pid=30320901, pid=30932501, pid==3101602, pid==3101801) developments with xtline. 1*c) 2*Stability of the relationship status 3xttab married 6.8. Fixed Effects Estimation 289 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 The coefficients of pgexpft and pgexpft^2 remain significant, whereas the coefficient for married is no longer significant. 1graph twoway (func y =_b[pgexpft]*x+_b[c.pgexpft#c.pgexpft]*x*x,␣ ˓→range(0 40)) 296 Chapter 6. Working with SOEP Data SOEP Survey Paper 1261 SOEPcompanion, Release 2023 The graph shows that the effects of the labor market experience decrease after approximately 15 years of professional experience. f) Now estimate the model from task 5e) with longitudinal section weights. Why is the number of cases now significantly smaller? Why could the coefficient of “pgbilzeit” have changed? Tip: Create your own longitudinal person weights, e.g., longitudinal person weight from wave A to wave D. Take the starting wave cross-sectional weight (aphrf) and multiply through by each following wave staying factor, as in the following example: gen adphrf=aphrf*bpbleib*cpbleib*dpbleib Since you are looking at the period 2012-2016, you must create a suitable longitudinal weight. To do this, use the phrf dataset from the RAW subdirectory. Apply the required variables to your analysis dataset and generate your periodrelated longitudinal section weight. To understand the structure of the data distribution file and the location of the different datasets, visit the section Data Distribution File. For more information about the weighting datasets and other survey datasets, see the section Survey Data. 1*f) Fixed Effects weighted 2global MY_IN_PATH2 "\\hume\rdc-prod\complete\soep-core\soep.v33.2\stata_ ˓→en\" 3rename pid persnr 4merge m:1persnr using "${MY_IN_PATH2}/phrf.dta", nogen keep(master␣ ˓→match) keepus(bcphrf bdpbleib bepbleib bfpbleib bgpbleib) 5gen wlong =bcphrf*bdpbleib*bepbleib*bfpbleib*bgpbleib 6label variable wlong "Weighting BC-BG" 7rename persnr pid Now estimate the model from 5e) and use the created weight. 1xtreg lwage married c.pgexpft##c.pgexpft pgbilzeit i.syear [pw=wlong],␣ ˓→fe vce(cluster pid) 6.8. Fixed Effects Estimation 297 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 The number of observations is now much smaller. The effect of pgbilzeit is greater than before. Pgbilzeit has a lower effect in the wlong==0 group, where the return is different for each additional educational year. People in the wlong===0 group may not get the returns on additional education they expected on the local labor market and may therefore move -> higher dropout probability. Last change: Mar 28, 2023 In order to gain the best possible insight into how to work with the various regional data on the SOEP, we recommend the following exercises: 298 Chapter 6. Working with SOEP Data SOEP Survey Paper 1261 SOEPcompanion, Release 2023 6.9 Working with SOEP Regional Data SOEP offers diverse possibilities for regional and spatial analysis. With the anonymized regional information on SOEP respondents’ (households’ and individuals’) place of residence, it is possible to link numerous regional indicators on the levels of the federal states (Bundesländer), spatial planning regions, districts, and postal codes with the data on the SOEP households. However, specific security provisions must be made due to the sensitivity of the data under data protection law. Accordingly, data users are not allowed to give any information in their analyses that could indicate, for instance, the city or district in which respondents reside. The data nevertheless provide valuable background information for regional analysis. For more information and to access the data, see Regional Data Assume that for your research project, you want to measure current (2016) urban-rural differences in the population. You are particularly interested in the differences in interest in politics and the different satisfaction variables provided by the SOEP. You also want to take into account demographic differences in gender and age. To be able to evaluate the potential of the data for your project, you first need an overview. For regional analysis, for example, the municipal size classes from the regional data are suitable. Create an exercise path with four subfolders: Example: •H:/material/exercises/do 6.9. Working with SOEP Regional Data 299 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 •H:/material/exercises/output •H:/material/exercises/temp •H:/material/exercises/log These are used to store your script, log files, datasets, and temporary datasets. Open an empty do-file and define your paths with globals: 1*********************************************** 2*Set relative paths to the working directory 3*********************************************** 4global AVZ "H:\material\exercises" 5global MY_IN_PATH "\\hume\rdc-prod\complete\soep-core\soep.v33.2\stata_ ˓→en\" 6global region "\\hume\soep-region\DATA\soep33_de\" 7global MY_DO_FILES "$AVZ\do\" 8global MY_LOG_OUT "$AVZ\log\" 9global MY_OUT_DATA "$AVZ\output\" 10 global MY_OUT_TEMP "$AVZ\temp\" The global “AVZ” defines the main path. The main paths are subdivided using the globals “MY_IN_PATH”, “MY_DO_FILES”, “MY_LOG_OUT”, “MY_OUT_DATA”, “MY_OUT_TEMP”. The global “MY_IN_PATH” contains the path to the data you ordered. a) Prepare a dataset for cross-sectional analysis covering the survey year 2016 (wave bg). To perform your analysis, you need different SOEP variables. The SOEP offers various options for a variable search: •Search the questionnaires for useful variables (for more information, see the section Variable Search with Questionnaires) •Find a suitable variable in the topic list on paneldata.org (for more information, see the section Topic Search with paneldata.org) •Search for a suitable variable using a search term in paneldata.org (for more information, see the section Variable Search with paneldata.org) •Use the documentation provided by the generated variables (for more information, see the section Documentation on Generated Data) Your source file should contain the following variables: •Permanent Individual ID "persnr" •Original Household Number "hhnr" •Current Wave Household Number "bghhnr" •The Sex of the Person "sex" •Year of Birth "gebjahr" •Survey Status 2016 "bgnetto" •Sample Membership 2016 "bgpop" •Weighting Factor 2016 "bgphrf" •Satisfaction With Health "bgp0101" •Satisfaction With Sleep "bgp0102" •Satisfaction With Work "bgp0103" 300 Chapter 6. Working with SOEP Data SOEP Survey Paper 1261 SOEPcompanion, Release 2023 •Satisfaction With Housework "bgp0104" •Satisfaction With Household Income "bgp0105" •Satisfaction With Personal Income "bgp0106" •Satisfaction With Dwelling "bgp0107" •Satisfaction With Amount Of Leisure Time "bgp0108" •Satisfaction With Child Care "bgp0109" •Satisfaction With Family Life "bgp0110" •Satisfaction With Social Life "bgp0111" •Satisfaction with Democracy "bgp0112" •Political Interest "bgp143" •Current Sample Region "bgsampreg" •Federal State "bgbula" •Spatial Category by BBSR "bgregtyp" •Municipal Class Sizes “ggk” Use the key variables from the ppath.dta dataset as your starting file. 1use hhnr persnr bghhnr sex gebjahr bgnetto bgpop using ${MY_IN_PATH}\ ˓→ppfad.dta, clear Attention: Please note that since version 34 (v34), PPFAD can be found in the subdirectory “Raw” of the data distribution file. The following exercises are done with version 33.1 (v33.1), where the tracking file was named PPFAD. Keep people who completed a questionnaire in 2016 and lived in a private household. 1* Keep people who completed a questionnaire in 2016 and live in a␣ ˓→private household 2keep if bghhnr>0 & inrange(bgnetto, 10, 29) & inlist(bgpop, 1, 2) 3keep hhnr persnr bghhnr sex gebjahr bgnetto bgpop 4merge 1:1 persnr using ${MY_IN_PATH}\phrf.dta, keep(match master)␣ ˓→keepusing (bgphrf) nogenerate 5tempfile ppfad 6save `ppfad' Prepare the different datasets bgp, bghbrutto, regionl 1* Prepare dataset bgp 2use ${MY_IN_PATH}\bgp.dta, replace 3keep persnr hhnr bghhnr bgp01* bgp143 4tempfile bgp 5save `bgp' 6 7* Prepare dataset bghbrutto 8use ${MY_IN_PATH}\bghbrutto.dta, replace 9keep hhnr bghhnr bgsampreg bgbula bgregtyp (continues on next page) 6.9. Working with SOEP Regional Data 301 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 (continued from previous page) 10 tempfile bghbrutto 11 save `bghbrutto' 12 13 * Prepare dataset regionl 14 use ${region}\regionl_v33.dta, replace 15 keep if syear==2016 16 keep syear hhnr hhnrakt ggk 17 rename hhnrakt bghhnr 18 tempfile regionl 19 save `regionl' Merge all datasets. 1* Merge all datasets 2use `ppfad' 3merge 1:1 persnr using `bgp', keep(match master) nogenerate 4merge m:1 bghhnr hhnr using `regionl', keep(match master) nogenerate 5merge m:1 bghhnr hhnr using `bghbrutto', keep(match master) nogenerate Recode negative values as missings. 1*Recode negative values into missings 2mvdecode sex gebjahr bgp01*bgp143,mv(-5/-1) Categorize the municipal class sizes from the SOEP regional dataset. 1*Categorize community class size 2gen ggk_cat=. 3replace ggk_cat=-1if ggk==-1 4replace ggk_cat=1if ggk==1|ggk==2 5replace ggk_cat=2if ggk==3 6replace ggk_cat=3if ggk==4|ggk==5 7replace ggk_cat=4if ggk>5&ggk<=7 8 9lab var ggk_cat "Community Size categorised" 10 lab def ggk_cat -1"No information" 1"<=5000" 2"5001 - 20000" 3"20001␣ ˓→- 100000" /// 11 4">100000" 12 lab val ggk_cat ggk_cat Generate an age variable. 1*Generate age variable 2gen alter=2016-gebjahr if gebjahr >0 3gen alter_cat=1if alter<=20 4replace alter_cat=2if alter>20 &alter<=30 5replace alter_cat=3if alter>30 &alter<=65 6replace alter_cat=4if alter>65 &alter<=120 7 8lab var alter "age" 9lab var alter_cat "age categorized" 10 lab def alter_cat 1"<=20" 2"21-30" 3"31-65" 4">65" 11 lab val alter_cat alter_cat 302 Chapter 6. Working with SOEP Data SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Categorize a federal states variable. 1*Categorize federal states 2gen bgbula_cat=. 3*Schleswig-Holstein +Hamburg 4replace bgbula_cat=1if bgbula==1|bgbula==2 5*Lower Saxony +Bremen 6replace bgbula_cat=2if bgbula==3|bgbula==4 7*Mecklenburg Western Pomerania +Brandenburg 8replace bgbula_cat=3if bgbula==13 |bgbula==12 9*Saarland +Rhineland Palatinate 10 replace bgbula_cat=4if bgbula==7|bgbula==10 11 *Northrhine-Westphalia 12 replace bgbula_cat=5if bgbula==5 13 *Hesse 14 replace bgbula_cat=6if bgbula==6 15 *Baden-Württemberg 16 replace bgbula_cat=7if bgbula==8 17 *Bavaria 18 replace bgbula_cat=8if bgbula==9 19 *Berlin 20 replace bgbula_cat=9if bgbula==11 21 *Saxony 22 replace bgbula_cat=10 if bgbula==14 23 *Saxony-Anhalt 24 replace bgbula_cat=11 if bgbula==15 25 *Thuringia 26 replace bgbula_cat=12 if bgbula==16 27 28 lab var bgbula_cat "Federal states categorized" 29 lab def bgbula_cat 1"Schleswig-Holstein/Hamburg" 2"Lower Saxony/Bremen ˓→"3"Mecklenburg Western Pomerania/Brandenburg" /// 30 4"Saarland/Rhineland Palatinate" 5"Northrhine-Westphalia" 6"Hesse" /// 31 7"Baden-Wuerttenberg" 8"Bavaria" 9"Berlin" 10 "Saxony" 11 "Saxony- ˓→Anhalt" 12 "Thuringia" 32 lab val bgbula_cat bgbula_cat 33 drop bgbula 34 rename bgbula_cat bgbula Put the variables in your preferred order and save your dataset. 1* Order demography and identifiers first 2order persnr hhnr bghhnr syear sex gebjahr alter alter_cat bgsampreg␣ ˓→bgbula ggk /// 3ggk_cat bgregtyp 4 5save ${MY_OUT_DATA}\zeit_online.dta, replace b) You want to get an initial overview of regional differences in satisfaction with various aspects of life. Use the variable bgsampreg and cross-stabilize the variable with all satisfaction variables to identify differences between East and West Germany, display the absolute and relative frequencies. To save the tables, save them in a log file. 6.9. Working with SOEP Regional Data 303 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 1******************************************************************************** 2capture log close 3log using "${MY_LOG_OUT}\satisfaction.log", replace 4 5* Life satisfaction 6 7local varlist bgp0101 bgp0102 bgp0103 bgp0104 bgp0105 bgp0106 bgp0107␣ ˓→bgp0108 /// 8bgp0109 bgp0110 bgp0111 bgp0112 9foreach x of local varlist { 10 tab bgsampreg `x'[aw= bgphrf] , row 11 } To view all tables, look at your generated log file. c) Now take a closer look at satisfaction with various aspects of life with the help of SOEP regional data. Use the municipal size classes. Create a table showing satisfaction with different aspects of life and highlighting differences by sex, age, municipal size class, and federal state. 1foreach x of local varlist { 2* Tabulation of satisfaction by municipal size class and federal state 3table `x'sex alter_cat, by(bgbula ggk_cat) contents(freq) column row␣ ˓→stubwidth(20) cellwidth(8) csepwidth(2) nomissing 4* Tabulation of satisfaction by municipal size class 5table `x'sex alter_cat, by(ggk_cat) contents(freq) column row␣ ˓→stubwidth(20) cellwidth(8) csepwidth(2) nomissing 6* Tabulation of satisfaction by federal state (continues on next page) 304 Chapter 6. Working with SOEP Data SOEP Survey Paper 1261 SOEPcompanion, Release 2023 (continued from previous page) 7table `x'sex alter_cat, by(bgbula) contents(freq) column row␣ ˓→stubwidth(20) cellwidth (8) csepwidth(2) nomissing 8} To view all tables, look at your generated log file. As you can see, SOEP regional data can be used to analyze variables at the lowest regional levels. 6.9. Working with SOEP Regional Data 305 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 (continued from previous page) ## 8 13.281110837709 52.4473851451014 FU Berlin <NA> ## 9 13.1725456943567 52.4300072503147 Wannsee <NA> ## 10 13.2128459704725 52.5411476127425 Zitadelle Spandau <NA> ## 11 13.5727300494297 52.4438163134271 Schloss Köpenick <NA> ## 12 13.2795658406826 52.5080182126806 Zentraler Omnibus Bahnhof <NA> POI <- POI[, -4] Because this data contains latitude and longitude already, we can simply transform it to a spatial dataset using st_as_sf and the CRS has to be assigned to it. POI <- st_as_sf(POI, coords =c("X","Y"), crs =4326) POI ## Simple feature collection with 12 features and 1 field ## geometry type: POINT ## dimension: XY ## bbox: xmin: 13.17255 ymin: 52.43001 xmax: 13.57273 ymax: 52. ˓→55801 ## geographic CRS: WGS 84 ## First 10 features: ## Objekt geometry ## 1 Brandenburger Tor POINT (13.3777 52.51628) ## 2 Hauptbahnhof POINT (13.3693 52.52503) ## 3 DIW Berlin POINT (13.38861 52.51217) ## 4 Mulecule Man POINT (13.45893 52.49683) ## 5 Flughafen Tegel POINT (13.28873 52.55801) ## 6 Tempelhofer Feld POINT (13.40055 52.47928) ## 7 Hufeisensiedlung POINT (13.44846 52.44821) ## 8 FU Berlin POINT (13.28111 52.44739) ## 9 Wannsee POINT (13.17255 52.43001) ## 10 Zitadelle Spandau POINT (13.21285 52.54115) 6.10.3 Transformations To be able to work with all three data sets (States,SOEP, and POI) we have to make sure all have the same CRS. Using st_transform we can reproject the data sets to have the same EPSG-code (common_crs). common_crs <- 25832 SOEP <- st_transform(SOEP, crs =common_crs) SOEP <- SOEP[SOEP$erhebj == 2005, ] # only use 2005 SOEP data POI <- st_transform(POI, crs =common_crs) # States <- st_transform(States, crs = common_crs) aleady correct crs Sometimes it might be easier for you to work in other programs and you wish to have latitude and longitude data. In this case you can use st_coordinates to transform point geometries into (x,y) coordinates. The below example first transforms the coordinates for the households 1 to 5 in the SOEP data into latitude and longitude data and then creates the (x,y) coordinates from the point geometry. 312 Chapter 6. Working with SOEP Data SOEP Survey Paper 1261 SOEPcompanion, Release 2023 soep5_lat_lon <- st_transform(SOEP[1:5,], 4326) st_coordinates(soep5_lat_lon) ## X Y ## 1 6.94659 51.12465 ## 2 13.36828 52.48428 ## 3 13.40601 52.39064 ## 4 8.70727 49.03966 ## 5 13.43205 52.48811 6.10.4 Plotting Spatial Data Besides being able to look up coordinates or objects in google maps or OpenStreetMap, we can use the ggplot2 package included in the tidyverse package for displaying the data. The package provides the geom_sf function to easily plot the data. # subsetting and plotting the data States %>% # use the states data filter(GEN == 'Berlin')%>% # filter for Berlin only ggplot() +# plot base geom_sf(fill ='white')+# add the polygon for Berlin and fill it␣ ˓→whtie geom_sf(data =POI) +# add the POI data geom_sf_text(data =POI, aes(label =Objekt), nudge_y =-1000, check_overlap =TRUE)+# overlapping labels will not be␣ ˓→displayed xlab('')+ylab('') 6.10. Working with spatial data in R 313 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 6.10.5 Frequently Used Operations This section will provide an overview of some frequently used operations when working with spatial data. The dataset SOEP contains some fake coordinates of household addresses we will work with throughout the examples. Finding Households in a Specified Area Suppose we are interested in identifying all the SOEP households located in Berlin. The corresponding polygon for Berlin is provided in the States dataset. Because we are interested in Berlin only, we save the polygon in an own object BE. The function st_contains identifies the row-index in the SOEP dataset that fall within the polygon BE and returns a list (soep_in_berlin). For checks along the way we can look at plots of the data. BE <- States[States$GEN == 'Berlin', ] BE %>% select(GEN, BEZ) ## Simple feature collection with 1 feature and 2 fields ## geometry type: MULTIPOLYGON ## dimension: XY ## bbox: xmin: 777974.1 ymin: 5808837 xmax: 823510.5 ymax:␣ ˓→5845580 ## projected CRS: ETRS89 / UTM zone 32N ## GEN BEZ geometry ## 11 Berlin Land MULTIPOLYGON (((802831.7 58... 314 Chapter 6. Working with SOEP Data SOEP Survey Paper 1261 SOEPcompanion, Release 2023 soep_in_berlin <- st_contains(BE, SOEP) soep_in_berlin ## Sparse geometry binary predicate list of length 1, where the␣ ˓→predicate was `contains' ## 1: 2, 3, 5, 6, 8, 9, 10, 11, 12, 13, ... SOEP_BE <- SOEP[unlist(soep_in_berlin), ] BE %>% ggplot() +geom_sf(fill ='white')+geom_sf(data =SOEP_BE) Distances / Areas To compute distances the function st_distance can be provided a single dataset with n rows providing a n x n matrix of distances of the geometries contained in the data (dist_m). The unit of the distance returned depends on the CRS. In the example provided below the distances of the POIs to the location of the DIW are given in meters. Providing the function a second dataset of m rows will create a n x m distance matrix (dist_soep_poi). The created object is a matrix with 529 rows (SOEP households in Berlin) and 12 columns (POIs in Berlin). When computing distances on large data sets it might be helpful to subset the data, because the distance of a household in Munich might be irrelevant to a research question focusing on Berlin or distances smaller than 5000m. According the the CRS, consider specifying the which argument. 6.10. Working with spatial data in R 315 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Areas can be computed for (multi-)polygons. The function st_area provides the corresponding information. # distances between the POIs dist_m <- st_distance(POI) rownames(dist_m) <- POI$Objekt colnames(dist_m) <- POI$Objekt # distance of POIs to DIW Berlin dist_m['DIW Berlin', ] ## Units: [m] ## Brandenburger Tor Hauptbahnhof ␣ ˓→DIW Berlin ## 870.8119 1941.2940 ␣ ˓→0.0000 ## Mulecule Man Flughafen Tegel ␣ ˓→Tempelhofer Feld ## 5074.7951 8488.2154 ␣ ˓→3751.7696 ## Hufeisensiedlung FU Berlin ␣ ˓→Wannsee ## 8202.7627 10269.1064 ␣ ˓→17307.5100 ## Zitadelle Spandau Schloss Köpenick Zentraler Omnibus␣ ˓→Bahnhof ## 12364.7837 14651.8215 ␣ ˓→7422.6480 # distance between each household and each POI dist_soep_poi <- st_distance(SOEP_BE, POI) dim(dist_soep_poi) ## [1] 529 12 # save distances in an object DIST <- as_tibble(dist_soep_poi) # add names names(DIST) <- str_c('distance_to_',str_remove(POI$Objekt, ' ')) # attach distances to data SOEP_BE <- bind_cols(SOEP_BE, DIST) # area covered by Berlin st_area(BE) ## 893060962 [m^2] Nearest Point To find the point or feature closest to another one the function st_nearest_feature will return a vector with indices of the nearest feature. In the example below we are looking for the households living closest to the POIs in Berlin. In the second step we compute the corresponding distances between the POI and the household. 316 Chapter 6. Working with SOEP Data SOEP Survey Paper 1261 SOEPcompanion, Release 2023 nearest_hh <- st_nearest_feature(POI, SOEP_BE) diag(st_distance(POI, SOEP_BE[nearest_hh, ])) ## Units: [m] ## [1] 256.5810 1195.8833 789.6812 392.5787 1913.8130 891.4404 1295. ˓→8621 ## [8] 520.3830 971.0417 364.4532 250.2833 1194.7832 BE %>% # polygon for Berlin ggplot() +geom_sf(fill ='white')+# plot the Berlin polygon geom_sf(data =POI) +# add the POIs geom_sf(data =SOEP_BE[nearest_hh, ], col ='red')# add the nearest␣ ˓→household Within Distance If your interest is about which households live within a certain distance to a specific point, st_is_within_distance lets you lookup geometries in a given distance (argument dist) and returns a list. The below example looks up households in a 5km distance of the Brandenburger Tor. The plot shows the 5km radius area in yellow, the location of the Brandenburger Tor (black dot) and than households within the distance (red dots). 6.10. Working with spatial data in R 317 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 r_5000 <- st_is_within_distance(POI[POI$Objekt == 'Brandenburger Tor',], SOEP_BE, dist =5000) r_5000 ## Sparse geometry binary predicate list of length 1, where the␣ ˓→predicate was `is_within_distance' ## 1: 1, 3, 6, 12, 21, 25, 28, 39, 54, 55, ... BE %>% # polygon for Berlin ggplot() +geom_sf(fill ='white')+# plot the Berlin polygon geom_sf(data =POI[POI$Objekt == 'Brandenburger Tor',]) +# add the POI geom_sf(data =SOEP_BE[unlist(r_5000), ], col ='red')+# add the␣ ˓→nearest household geom_sf(data =st_buffer(POI[POI$Objekt == 'Brandenburger Tor',], dist␣ ˓→=5000), alpha =0.2, fill ='yellow') The question cal also be asked the other way around: How many POIs are within a 5km radius of the SOEP households? This way the function st_is_within_distance returns a list of length equal to the number of SOEP households in Berlin (529). For each household the (row) index for the POI is given. To get the number of POIs in the 5km radius, we can simply ask for the length (the number of row indices) of each list-component. To get the according distances see section Distances / Areas 318 Chapter 6. Working with SOEP Data SOEP Survey Paper 1261 SOEPcompanion, Release 2023 poi_5000 <- st_is_within_distance(SOEP_BE, POI, dist =5000) poi_5000 ## Sparse geometry binary predicate list of length 529, where the␣ ˓→predicate was `is_within_distance' ## first 10 elements: ## 1: 1, 2, 3, 6 ## 2: (empty) ## 3: 1, 3, 4, 6, 7 ## 4: 5 ## 5: 2, 5, 12 ## 6: 1, 2, 12 ## 7: 10 ## 8: 5, 10, 12 ## 9: 8 ## 10: 5, 10, 12 N_POI <- as_tibble(sapply(poi_5000, length)) names(N_POI) <- 'n_poi_in_5km' SOEP_BE <- bind_cols(SOEP_BE, N_POI) Spatial joins When you are used to working with SOEP data you will have probably merged / joined data sets using the identifying variables (cid,hid,pid) and the survey year (syear) before. When you are working with spatial data you will have to choose one of the geometry predicate function provided by the sf package. The default is a left join of the two data sets using st_intersects as the geometry predicate function for joining. You can however change this, for example, to join the nearest features, see section Nearest Point. In our example here, we join the nearest SOEP household to each of the points of interest. The geometry column here provides the coordinates from the POI data set. NEAR <- st_join(POI, SOEP, join =st_nearest_feature) NEAR ## Simple feature collection with 12 features and 3 fields ## geometry type: POINT ## dimension: XY ## bbox: xmin: 783630.5 ymin: 5817059 xmax: 810721.7 ymax:␣ ˓→5831749 ## projected CRS: ETRS89 / UTM zone 32N ## First 10 features: ## Objekt erhebj ID geometry ## 1 Brandenburger Tor 2005 75759 POINT (796986.8 5827473) ## 2 Hauptbahnhof 2005 69076 POINT (796358.6 5828411) ## 3 DIW Berlin 2005 75759 POINT (797754.3 5827062) ## 4 Mulecule Man 2005 75646 POINT (802628.5 5825649) ## 5 Flughafen Tegel 2005 73266 POINT (790677.7 5831749) ## 6 Tempelhofer Feld 2005 69820 POINT (798787.2 5823455) ## 7 Hufeisensiedlung 2005 69477 POINT (802251.6 5820202) ## 8 FU Berlin 2005 67568 POINT (790892.2 5819422) ## 9 Wannsee 2005 67923 POINT (783630.5 5817059) ## 10 Zitadelle Spandau 2005 68827 POINT (785646.9 5829572) 6.10. Working with spatial data in R 319 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Export Results To export your results you can use st_write to create a .csv file. When exporting your results pleace check the requirements here. path_export <- paste0('/home/',Sys.info()['user'], '/transfer/export/',␣ ˓→Sys.Date()) if(!file.exists(path_export)){ dir.create(path_export, recursive =TRUE) } st_write(SOEP_BE, file.path(path_export, 'Output_SOEP_BE.csv'), append =FALSE, overwrite =TRUE) README <- tibble(name =names(SOEP_BE)[-grep('geometry',names(SOEP_ ˓→BE))], description =c('erhebj', 'ID', 'distance (in meters) of household to␣ ˓→Brandenburger Tor', 'distance (in meters) of household to␣ ˓→Hauptbahnhof', 'distance (in meters) of household to␣ ˓→DIW-Berlin', 'distance (in meters) of household to␣ ˓→Mulecule Man', 'distance (in meters) of household to␣ ˓→Flughafen Tegel', 'distance (in meters) of household to␣ ˓→Tempelhofer Feld', 'distance (in meters) of household to␣ ˓→Hufeisensiedlung', 'distance (in meters) of household to␣ ˓→FU Berlin', 'distance (in meters) of household to␣ ˓→Wannsee', 'distance (in meters) of household to␣ ˓→Zitadelle Spandau', 'distance (in meters) of household to␣ ˓→Schloss Köpenick', 'distance (in meters) of household to␣ ˓→Zentraler Omnibus Bahnhof', 'number of POIs within 5 km radius of␣ ˓→household')) write.csv(README, file.path(path_export, 'Output_SOEP_BE.csv'), row.names =FALSE) 320 Chapter 6. Working with SOEP Data SOEP Survey Paper 1261 SOEPcompanion, Release 2023 6.10.6 Complete Example Suppose you want to know which households of the SOEP from survey year 2011 live within a distance of 5000m to the following points of interest (POI): •Brandenburger Tor •Zitadelle Spandau •Wannsee Besides that, you want to know how far their distance to the corresponding POI is and which household lives closest to the corresponding POI. After computing the informations need you want to export the results for further use on the HAUSER server. # Global stuff # ~~~~~~~~~~~~ # packages library(here) library(sf) library(tidyverse) # global values survey_year <- 2011 distance <- 5000 # meter common_crs <- 25832 # Step 1: read the data # ~~~~~~~~~~~~~~~~~~~~~ # read polygons for Federal States States <- st_read(here('Daten','vg250_ebenen_0101'), layer ='VG250_LAN ˓→', quiet =TRUE) # read SOEP data SOEP <- st_read(here('Daten','soep_v29'), quiet =TRUE) # read POI data POI <- st_read(here('Daten','POIs_Berlin.csv'), quiet =TRUE) # Step 2: Transform data # ~~~~~~~~~~~~~~~~~~~~~~ # transform SOEP the data SOEP <- st_transform(SOEP, crs =common_crs) # transform POIs POI <- POI[, -4] POI <- st_as_sf(POI, coords =c("X","Y"), crs =4326) (continues on next page) 6.10. Working with spatial data in R 321 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Fig. 2: Figure 2: Connection with LAN available For each available server, two icons are displayed on the start screen at the top left, a red one and a blue one with the same name. See figure 3. The following two servers are currently available: 1. HAUSER: Access to the SOEP survey data, including connection to small-scale regional indicators (WITHOUT coordinates). 2. MORAN: Access to the coordinates of SOEP households, but without survey data. Access is only possible from RDC SOEP guest stations at DIW Berlin 328 Chapter 6. Working with SOEP Data SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Fig. 3: Figure 3: Icons to connect with the SOEP server Blue Icon: To connect to one of the two servers at RDC SOEP, first establish an open VPN connection by clicking on the blue icon for the server you would like to connect to. The icon in the lower right corner should then display the existing VPN connection. By clicking once on this icon, you can see the server’s IP address . See figure 4 Red Icon: Once you have established the VPN connection to the SOEP server, click once on the red icon to start your session. The server’s login window should appear, see figure 5. Enter the user name and password provided to you by RDC SOEP. 6.11. How to Use SOEP IGEL 329 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Fig. 4: Figure 4: Open VPN connection established Fig. 5: Figure 5: Login to the SOEP Server 330 Chapter 6. Working with SOEP Data SOEP Survey Paper 1261 SOEPcompanion, Release 2023 6.11.3 Working with SOEP DATA Starting programs •After you have logged in, a blank desktop will appear with a menu bar at the top. •In general, programs can be started by clicking on “activities” and then either by clicking on the icon or by typing the name of the program into the search field. •Users should inform the RDC SOEP team in advance about any additional ados in Stata or packages in R. These will be installed after checking. •Start Stata: Unfortunately, there is no automatic start icon for Stata, so you have to do the following: 1. Click on activities 2. Enter “terminal” in the search window 3. Start either “Terminal” or “XTerm”. 4. Enter the command “xstata-mp” into the terminal that has now appeared, and press the return key. Stata should now appear. •The following table shows which programs are installed and available for use on each server: Running time-consuming computing operation If a script needs more than one hour for calculation, the script should be run in the background. For long running scripts in R or Stata you should not use xstata or Rstudio. Executing your script •The R-script should be executed with the “Rscript” command in the terminal. •The STATA do-file should be executed with the “stata-mp do” command in the terminal. What should be used? Yo can start a session in tmux or you can use the command nohup. We suggest to use tmux. How to start your script? Open a terminal and change to the folder where the script is located with the command cd. Now you can execute your script using tmux as follows: 1. Start a tmux session and assign a name (*myname*) with the command tmux new -s myname. If later you forget the name, use the command: tmux list-session 2. Execute your script: In R (*yourscript.R*) / in STATA (*yourdofile.do*) 3. When logging in again, all running sessions can be listed with tmux list-session 4. This session (*myname*) could be restored after the terminal was closed with tmux attach-session -t myname 5. When your script is finished, please start your tmux session again with tmux attach-session -t myname and close your running session with exit 6.11. How to Use SOEP IGEL 331 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Program HAUSER MORAN Stata Yes -/- R/RStudio Yes Yes QGIS -/- Yes grass -/- Yes PostGis -/- Yes LibreOffice Yes Yes Emacs Yes Yes gnome-text-editor Yes Yes Nautilus (File manager) Yes Yes Using SOEP data and your own data •The latest version of the SOEP data is available at the following address directory path: HAUSER ~/soep-data/ or /import/SOEP-Regio/data/ MORAN ~/soep-data/ or /import/SOEP-GIS/data •You can store your own data and scripts in your personal home directory. ~/work/ Logging out •Use the icon in the upper right corner •click on your username and on logout. 6.11.4 Importing Scripts or External Data •You can send these data to the RDC team before your stay. Send it to SOEP. Please use the following website: cs-soep.diw.de •Before you send us your files (only data files, text files and tables), please put all files into a zip archive and name it as user-YYYY-MM-DD.zip (mustermann-2020-12-24). Please do not send ados, binaries or r packages in the zip file, ados or r packages will be installed centrally by the SOEP team. •As receiver for the data and scripts please use [email protected]. •Before you come to us, please send us the data to import early (2 days in advance) enough so that we have enough time to install it. •You will be able to find and use yout imported data here: /home/USER/transfer/import/ •You can read, write and save in your personal directory: /home/USER/work/ Attention: Because disk space is limited, we had to introduce the concept of quotas: •each user gets 10 GB of disk space •to display there is the quota command 332 Chapter 6. Working with SOEP Data SOEP Survey Paper 1261 SOEPcompanion, Release 2023 •the data remains on the server until the end of the project duration. •after the end of the project, the data is taken from the server and archived for 10 years. •it is possible to upload the data to the server again later with sufficient preparation time 6.11.5 Instructions for exporting from Hauser to user From a secure guest workstation at the SOEP Research Data Center, users can analyze SOEP data in combination with small-scale regional data. However, to provide users with this sensitive information, we have to carry out additional protective measures of both a technical and organizational nature. At a guest workstation at the SOEP Research Data Center, you work on a thin client from which you cannot export any data on your own. Below we describe how you can obtain the results of your analyses after they have been checked for anonymity. How can I take my results with me? In your transfer folder, you will find an import folder (containing your external data that have been imported into the system) and an export folder. 1. Create on the server ‘Hauser’ below directory ‘~/tranfer/export/’ a new subdirectory with a name as the current date in ISO-format: mkdir /home/USER/transfer/export/yyyy-mm-dd Eg.: You are user Jane Doe on the server ‘Hauser’ and today is February 29, 2021 jdoe@Hauser mkdir /home/jdoe/transfer/export/2021-02-29 We know there wasn’t a February 29th in 2021, but that’s just a format example 2. This folder should contain the following 1. The results that you definitely need to take with you (for formal criteria, see below) 2. A README file (as a .txt file, Word file, or Libre Office file) in which you briefly describe each file in the export folder 3. Please make sure that the README file is readable and that line breaks are used 3. Check your files •Before you make a request for an export please check your data structure with the OutputControl command. •Execute this command in the export folder you want to export. •This command is used to check whether the formal requirements of the files are met (more information in the chapter “Formal criteria for exporting files”). Change to the folder you want to export cd /home/USER/transfer/export/yyyy-mm-dd In the Terminal, enter the following command: OutputControl Check the Control_output_USER_yyyy_mm_dd.txt output in the folder Control in your export folder. •If you want to make a request for an export, the control file should not contain any warnings. •If you have any questions, please contact the SOEP hotline. 4. When your folder is complete, please send an e-mail to [email protected] with your export request Before you submit an export request, please check that your export is complete and ensure that the following criteria have been met: 6.11. How to Use SOEP IGEL 333 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Attention: Please read the following rules carefully. If you break the rules, you will not receive your export files. Formal criteria for exporting files: •Microdata sets at household or personal level will NOT be exported. •Only the outputs of analysis (tables, figures), syntax files, and log files will be exported: •Tables: –must be stored in the file format .csv –the maximum number of text files and tables is 200 •Figures: –must be saved in one of the following file formats: .png, .svg, .jpg, .tiff, .eps, .pdf •Text files (scripts or log files): –must be stored in one of the following file formats: .txt, .tex, .do, .r, .pdf, .log, .md –may have a maximum of 25,000 lines (a command to count these from a terminal for all .log files in a directory is wc -l *.log) – the maximum number of text files and tables is 200 ∗Please make sure that the files are readable and that line breaks are used •Please note that no special characters or spaces are used in file names. Please check if the files are really readable after creating. •An export request can only be made once a week Criteria for exporting results: •In principle, the results cannot allow any conclusions to be drawn as to which spatial planning region (or smallerscale geographic unit) a household or individual was or is part of. •No regional information (e.g., municipality code, district code, zip code ...) may be listed (e.g., using the list command in Stata) together with identifiers (e.g., individual ID number, household ID number) •When creating tables and figures, the minimum cell population must be kept at 10 if region-specific characteristics are used. Additional notes on export: •Since the export has to be checked manually, checking can take up to two or more weeks, of course depending on the number of files to be checked. •The export link sent to you will only be available for a specified period of time (at least two weeks). •To open the export link, use your guest access password. 334 Chapter 6. Working with SOEP Data SOEP Survey Paper 1261 SOEPcompanion, Release 2023 6.11.6 Data transfer from Moran to Hauser From the three servers of “SOEPgeo”, or the SOEP Research Data Center’s guest network, SOEP users can analyze geocoded data for scientific purposes on site at the SOEP Research Data Center. Researchers are first required to sign a data protection agreement, and a complete record is kept of all data access. The concept is to keep the geo-coordinates of SOEP households separate from the actual survey information throughout the entire process of analysis by data users. Only the coordinates and the survey year are needed generate topic-related indicators in a geographic information system (GIS; grass, qgis, and postgis are installed on Moran) or in the statistical package R, and no further information on either the household or household members. SOEP Research Data Center staff transfer indicators generated by users in a GIS. This prevents any possibility of users accessing the data. The key component of the data protection concept is that SOEP households’ geo-coordinates are kept separate from the survey information: •At no time do data users have simultaneous access to coordinates and survey data •Data users can only generate topic-related indicators on Moran, where the SOEP survey data are not accessible. •Data users can only analyze the topic-related indicators on Hauser, where the SOEP-household coordinates are not accessible. •Topic-related indicators that were generated based on household coordinates may only be analyzed on the Hauser server and may not be exported from there. The data user therefore has no simultaneous access to the SOEP survey data and the geo-coordinates of SOEP households. The results (exported Hauser results) may only be published in completely anonymous form. How do I initiate data transfer from Moran to Hauser? Attention: Please read the following rules carefully. If you break the rules, the data transfer cannot be executed. Steps to initiate data export by the SOEP Research Data Center: 1. Create a subdirectory in the export folder on Moran with the export date: mkdir /home/USER/transfer/export/yyyy-mm-dd 2. This folder should contain both the dataset to be exported and a corresponding README.csv: •dataset with generated indicators and ID (see below) •README.csv (see below) 3. Send an e-mail to [email protected] with the following information: •What input dataset was used for the coordinates? To ensure correct data transfer, we need to know what version of the data was used (e.g., v35) •What is the export file format? (.rds, .shp, .csv are permitted) (to save in dataframe in rds format please use saveRDS()) •What are the unique identifiers for the dataset? (e.g., ID & syear) Formal criteria for data transfer: The following criteria apply to exports: •The README.csv is a two-column .csv table –$name: column containing the variable names of the indicators to be exported (e.g., distance) –$description: short description of the respective variable (e.g., distance in meters to the next flood point for household i in year t (for the flood in 2002)) •The following applies to the dataset containing the indicators to be exported 6.11. How to Use SOEP IGEL 335 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 –Dataset must have the column/variable ID from the input dataset used –Permissible file formats: rds, shp, csv –Dataset otherwise only contains the indicators described in the README.csv file Additional notes on data transfer: •After the data transfer has taken place, the output (datasets, transfer scripts) will be stored in your transfer folder on Hauser, in a subdirectory of your import folder that is identified by date (/home/USER/transfer/import/fromMoran/yyyy-mm-dd) Section author: Jan Goebel <[email protected]> Last change: Jun 01, 2023 If you want to import the SOEP data as csv files with an older version of Stata, this exercise will help you. 6.12 Working with SOEP data in csv format SOEP offers the data in statistical program specific file formats (e.g.: Stata .dta) and also as comma-separated values FIle (csv). With these csvs you can read the non-formatted information directly into a statistical program of your choice. This example shows how to open SOEP data of data version v.36 in csv format with an old Stata version (12) and how to prepare the data in an efficient way. Create an exercise path with four subfolders: Example: •H:/material/exercises/do •H:/material/exercises/output •H:/material/exercises/temp •H:/material/exercises/log These are used to store your script, log files, datasets, and temporary datasets. Open an empty do-file and define the paths you created with globals: 1*********************************************** 2*Set relative paths to the working directory 3*********************************************** 4global AVZ "H:\material\exercises" 5global MY_IN_PATH "\\hume\rdc-prod\distribution\soep-core\soep.v35\csv" 6global MY_DO_FILES "$AVZ\do\" 7global MY_LOG_OUT "$AVZ\log\" 8global MY_OUT_DATA "$AVZ\output\" 9global MY_OUT_TEMP "$AVZ\temp\" 336 Chapter 6. Working with SOEP Data SOEP Survey Paper 1261 SOEPcompanion, Release 2023 The global “AVZ” defines the main path. The main paths are subdivided using the globals “MY_IN_PATH”, “MY_DO_FILES”, “MY_LOG_OUT”, “MY_OUT_DATA”, “MY_OUT_TEMP”. The global “MY_IN_PATH” contains the path to your ordered data. For the following script to work, the global “MY_IN_PATH” must contain the folder path to the SOEP csv files of all datasets. The csv files for each data set should always consist of three csvs. If we want to import and prepare the dataset jugendl in csv format, we need the following csv Files: •jugendl.csv •jugendl_variables.csv •jugendl_values.csv In the SOEP, the csv of each data set contains the variables as columns and their numerical values. Variables and Values csvs contain the variable labels and the value labels for the data set. First some packages for Stata have to be installed so that the process can start. 1* Import and Labeling of SOEP csv-Files 2clear 3set more off 4 5* Load ados 6capture which adolist 7if _rc==111{ 8ssc install adolist 9} 10 quietly adolist list 11 local allAdos `r(names)' 12 foreach package in fre labutil2 chardef labundef saveascii useold { 13 if !regexm("`r(names)'", " `package'") { 14 display as result "Paket " as error "`package'" as result " wird␣ ˓→versucht über SSC-Server zu installieren" 15 ssc install `package' 16 } 17 } Once the packages are installed, you will need to define the following functions to be able to label your dataset later. We define the function soeplabelsvars for linking the variables to the variable labels. 1* Assign German variable labels from *_variables.csv 2capture program drop soeplabelsvars 3program soeplabelsvars 4version 12 5syntax , varlabels(string) 6preserve 7insheet using "`varlabels'", clear names 8putmata varLab = (variable label_de) ,replace 9restore 10 foreach variable of varlist * { 11 label variable `variable'"" 12 } 13 14 mata: st_local("n", strofreal(rows(varLab))) 15 forvalues i = 1/`n'{ 16 mata: st_local("varName",varLab[`i',1]) (continues on next page) 6.12. Working with SOEP data in csv format 337 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Display the life satisfaction and limit the variable from 0 to 10 where 10 is very satisfied and 0 is very dissatisfied. 1tab p11101 if p11101>=0&p11101<=10 Sort and sum the dataset by household size. Limit the variable life satisfaction from 0 to 10. 1bysort d11106:sum p11101 if p11101>=0&p11101<=10 &d11106==d11106 344 Chapter 6. Working with SOEP Data SOEP Survey Paper 1261 SOEPcompanion, Release 2023 With 8.4, the highest mean score for life satisfaction is among households with 11 people. With 6.5,the lowest mean score for life satisfaction is among households with 16 people. This means that households with 11 household members are more satisfied than households with 16 people. After the analysis save the dataset. We need it for the next exercise. 1save "${MY_OUT_DATA}hgendata.dta", replace 6.13. How to Merge SOEP Data in Stata 345 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 6.13.3 m:1 merge – many-to-one on key variables Determine the extent to which life satisfaction in 2019 depends on whether the person is a main tenant, subtenant, owner or lives in a nursing home. First, open the desired master dataset we just generated. Merge the using dataset HL, which includes all variables of the household questionnaire over time. Because of different levels of analysis it is a m:1 merge. Use two key variables hid and syear. The option keep(match) keeps only the observations obtained in both datasets (or _merge==3). The option nogenerate suppresses the generation of the variable _merge. 1use "${MY_OUT_DATA}hgendata.dta", clear 2merge m:1hid syear using "${MY_IN_PATH}hl.dta", keep(match) nogenerate We have 49,888 merged observations. Display the variable life satisfaction limited to the 0 to 10 where 10 is very satisfied and 0 is very dissatisfied. 1tab p11101 if p11101>=0&p11101<=10 The variable hlf0001_h shows whether the individuals renting, leasing or owning the apartment or lives in a retirement home. Limit the variable to the values from 1 to 4. 1tab hlf0001_h if hlf0001_h>=1&hlf0001_h<=4 346 Chapter 6. Working with SOEP Data SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Sum the life satisfaction for each element of the variable hlf0001_h. 1foreach hlf0001_h in 1 2 3 4 { 2sum p11101 if p11101>=0 & p11101<=10 & hlf0001_h==`hlf0001_h' 3} Owners of apartments have the highest life satisfaction score with a mean of 7.8, while people living in nursing homes have the lowest life satisfaction score with a mean of 6.7. After the analysis save the dataset. 1save "${MY_OUT_DATA}hlgendata.dta", replace 6.13. How to Merge SOEP Data in Stata 347 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 6.13.4 joinby m:m specifies a many-to-many merge and is not a good idea. In an m:m merge, observations are matched within equal values of the key variable(s), with the first observation being matched to the first, the second, to the second, and so on. If the master and using datasets have an unequal number of observations within the group, then the last observation of the shorter group is used repeatedly to match with subsequent observations of the longer group. Thus m:m merges are dependent on the current sort order — something which should never happen. That is why you use a joinby command. joinby is similar to merge but forms all combinations of the observations where it makes sense. Consider the joinby command in the context of an example. For this we took segments from two datasets of the SOEP: ARTKALEN and BIOMARSM. Both are spell data. The following example is taken from the documentation “Working with spell data” , which can be downloaded here Spell Data Open an empty do-file and define your paths with globals. Globals are useful to import and export data. 1global path "H:\Merge-Übung\examples\" 2global MY_DO_FILE "${path}example_do_files\" 3global MY_IN_PATH "${path}example_input_data\spell_to_spell\" 4global MY_OUT_PATH "${path}example_output_data\" 5global MY_TEMP_PATH "${path}example_temp_data\" Our goal is to enrich information of one spell dataset by introducing information from the other dataset to model processes over time of two kinds. In that case, employment trajectories and their effect on the transition into marriage. We are dealing with two variables: employment status and marital status. The ARTKALEN dataset consists of 7 variables and 3 observations. We observe only one person. The BIOMARSM dataset consists of 7 variables and 2 observations. We also observe only one person. Before combining spell datasets, we prepare the two datasets separately. The preparation of both spell datasets, the master and using one, follow the exact same structure: rename variables (spelltyp to employment status and marital status), unfold each spell into subspells of duration of a single month, and delete variables we don`t need. After preparing the dataset, ARTKALEN consists of 14 variables and 12 observations. 348 Chapter 6. Working with SOEP Data SOEP Survey Paper 1261 SOEPcompanion, Release 2023 After preparing the dataset, BIOMARSM consists of 14 variables and 9 observations. Both datasets are saved and sorted by the unique personal identification numbers and the begin date of each spell. Using both identifiers we combine the datasets. You can see the time course and status of an individual. 1use ${MY_TEMP_PATH}\spelldata_1.dta, clear 2joinby persnr begin using ${MY_TEMP_PATH}\spelldata_2.dta,␣ ˓→unmatched(both) update As a result, we see a combination of the first two datasets. It is about an observing person and in each row we see his employment status and marital status in a certain period of his life. In this respect we can observe how his employment status changes on the transition to marriage. If your data has the same structure as the example, you should combine those datasets with the joinby command. 6.13. How to Merge SOEP Data in Stata 349 SOEP Survey Paper 1261 CHAPTER SEVEN WORKING WITH SOEP DOCUMENTATION 7.1 Variable Search with Questionnaires If you come across a variable in the dataset whose variable content is unclear, you should always check whether there is a suitable questionnaire for the dataset. Under Original Core Data you can see whether the datasets correspond to a survey instrument. The related questionnaires can be found here: Questionnaires Example: Working on a research project, you come across the variable bjh_16_04 with the German label “Auto: Gründe” (Car: Reasons) and the English label “Reason for No Car in Household” Unfortunately, it is unclear what exactly this variable represents. You should refer to the questionnaires for the complete question and possible filter instructions. Example Variable: bjh_16_04: Wave “bj” (Survey Year 2011); household questionnaire (“h”), question number 16, item 4 Open Questionnaires The variable “bjh_16_04” can be found in the questionnaires for 2019. Select the survey year and questionnaire by using the filter “Year” and “Type of Questionnaire” and download the household questionnaire. 350 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Search the variable “bjh_16_04” in the questionnaire. Since you are already in the correct questionnaire, you must now search for question 16. To understand which information the variable “bjh_16_04” contains, you have to deal with the question. For each answer category, respondents should indicate whether or not the shown items apply to the household. If the item does not apply, respondents must answer an additional question about the reasons. Both questions should be understood as separate variables. E.g. the variable “bjh_16_01” indicates whether an internet connection is available in the household. The reasons why there is no internet in the household can be found in the variable “bjh_16_02”. The variable “bjh_16_03” shows whether a car is present in the household and the variable “bjh_16_04” shows reasons why no car is present in the 7.1. Variable Search with Questionnaires 351 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 household. By looking into the questionnaire, the variable is now easier to understand. The variable “bjh_16_04” only contains people who do not have a car in their household and shows the reasons given. Last change: Mar 28, 2023 7.2 Variable Search with paneldata.org Paneldata.org also allows you to search for variables and to find more information about generated variables. It offers comprehensive frequency counts, chronologies of variables, cross-study variable linkage via concepts, a syntax generator, and a topic list for content search in the SOEP. Example Variable: bbh5508: Wave “bb” (Survey Year 2011); household questionnaire (“h”), question number 55, item 8 Open Paneldata Select the study SOEP-Core. The SOEP-Core overview contains important general information about the study, e.g., data access, survey method, questionnaires, themes, terms for missing codes, all available datasets in the study and metadata-based questionnaires. To search for a variable, a dataset, or a publication, simply enter the desired search term in the search bar. 352 Chapter 7. Working with SOEP Documentation SOEP Survey Paper 1261 SOEPcompanion, Release 2023 To obtain the desired results, you will need to input specific information. The results window displays all search results. You will see that the variable “bbh5508” originates from SOEP-Core data and can be found in the dataset “bbh” (survey year 2011). If your search is not so specific, you can also search by keywords. We are still interested in the topic “car”. To better limit the 10000 results, the filter options on the left and on the top should be used. We are looking for variables from the “SOEP-Core” datasets. The search results should be limited with the filter options. Which survey years are of interest to me, do I want to work with original data or generated data? For more information about the different datasets in SOEP-Core, see the section Data Distribution File. Should the variable I am looking for be at household level or at individual level? 7.2. Variable Search with paneldata.org 353 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Now enter the variable you are looking for in the search bar at the top right and click on the variable of interest. You will be directed to the variable overview, where you will find detailed information on the variable. Paneldata.org offers a variety of search options to fit the user’s search needs. 7.3 Topic Search with paneldata.org To provide an overview of the various topics in the SOEP, the variables have been grouped together on paneldata.org by topic. If you are looking for your research variables and do not want to check all datasets or questionnaires, the topic search on paneldata.org may help. Open Paneldata and select the main study SOEP-Core. The upper navigation bar leads you to the Topics area. Click on Topics and look at the list of variables. Select a topic that corresponds to your research interest, and a more detailed list of sub-topics will appear under the 360 Chapter 7. Working with SOEP Documentation SOEP Survey Paper 1261 SOEPcompanion, Release 2023 main topic heading. For example, if you are interested in different types of satisfaction, click on the topic “attitudes, values, and personality”. Underneath it, you will find the sub-topic “personality”. Suppose you are interested in health satisfaction. If you have found a suitable sub-topic, click on “show all the related variables”. All variables that fall under this topic will be displayed. 7.3. Topic Search with paneldata.org 361 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 The paneldata topic list has three possible functions for each sub-topic. You can display all variables that belong to a sub-topic. In the future, paneldata will also display the texts of the questions from the SOEP questionnaires in which the variables in that sub-topic appear. Paneldata also allows you to keep variables from a sub-topic in a variable basket. The chapter Syntax Generator on paneldata.org explains in detail how to use the basket in your research and what possibilities this offers. Click on one of the variables to see the variable overview. 362 Chapter 7. Working with SOEP Documentation SOEP Survey Paper 1261 SOEPcompanion, Release 2023 If you click on the concept of a variable, you will get to the concept overview. Concepts in SOEP are used to link variables with the same content. The concepts can even be used to link variables with the same content across studies. 7.3. Topic Search with paneldata.org 363 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 The concept overview displays the study- and wave-specific variables with this concept. The concept allows you to determine whether the variable you are looking for is also available and comparable across studies. In the column “Study” you can see which studies have the same variable linked by concept. The label of the respective variable is also displayed in the “Label” column. The column “path” shows the wave name of the variable. By clicking on the label, you will get to the overview of variables with all of the relevant information. The “Object” column in the concept overview shows you the type of information displayed. 364 Chapter 7. Working with SOEP Documentation SOEP Survey Paper 1261 SOEPcompanion, Release 2023 In addition to the variables linked by concept, you can find the relevant questions in the concept overview. Questions are displayed in the “Object” column with question. Without having to open the questionnaire, you can read the question and identify possible differences. Click on the desired question and you will be taken to the question display. 7.3. Topic Search with paneldata.org 365 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Attention: To find out the exact wording of the question and possible filter structures, a variable search in the questionnaires is necessary. The question display in Paneldata only provides a quick overview. In the question overview, you can navigate through the questionnaire using the “next question” and “previous question” buttons. The “Instrument” section shows the position of the question in the questionnaire, the survey year, and links to the metadata-based survey instrument. Click on the survey instrument “Questionnaire 2011”. 366 Chapter 7. Working with SOEP Documentation SOEP Survey Paper 1261 SOEPcompanion, Release 2023 The survey instrument used in the SOEP-IS study in 2011 is now displayed. You can navigate through the questionnaire in this overview. The search bar allows you to search for research-relevant terms. Click on the question to access the question display. Last change: Mar 28, 2023 7.4 Documentation on Generated Data SOEP-Core contains a wide range of generated variables and datasets. To facilitate data use, we generate a large number of variables in the process of data preparation and release them with the SOEP-Core data. To make the generation process transparent to users, we provide comprehensive documentation on the numerous generated datasets and variables. For an overview, see our Documentation on Generated Data Example: A number of frequently used variables are provided in SOEP as “generated variables” (e.g., the datasets $PGEN and $HGEN). These variables are checked for consistency across waves. The documentation can be used to answer the following questions: a) Which variable gives the highest school-leaving certificate attained by individuals surveyed in 2007? To search for the variable that provides this information, open Paneldata , click on the search button and the tab “Variables”, then enter “school leaving degree” in the search bar. Specify your search by adjusting the filter settings as follows: •study: soep-core •Conceptual dataset: Generated (raw folder) 7.4. Documentation on Generated Data 367 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 •analysis unit: individual •period: 2007 All variables could contain the information you are looking for. Since almost all variables in the search result come from the generated “xpgen” dataset, the documentation for the $pgen dataset should be used. Visit the Documentation of SOEP-Core Page and enter the search term pgen in the search field. Alternatively, you can also use the filters and select “Data Documentations”: 368 Chapter 7. Working with SOEP Documentation SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Now select the documentation of the required version of pgen The table of contents on the left gives you a classification of the dataset by topics. To find the variable you are looking for, select topic area 10. 7.4. Documentation on Generated Data 369 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 For a general introduction to SOEPhelp, type in the command help soephelp. Here you will find a detailed explanation of the Stata.ado and the different ways to use it. The .ado is available in German and English. With the command soephelp, using wave specific datasets (subdirectory raw), you receive a basic description of the dataset as well as a list of samples contained in it, including the instruments corresponding to the sample. By clicking on the provided links, you will get to the respective questionnaires or to the dataset on paneldata. 376 Chapter 7. Working with SOEP Documentation SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Using soephelp in longitudinal datasets, you also receive a basic description as well as a list of wave-specific datasets that are used to generate the longitudinal version. 7.5. Working with SOEPhelp 377 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 If you enter the command soephelp <variable> in a wave-specific data set, you will get detailed information about the variable in question. The question asked in the questionnaire is displayed as well as the samples and instruments in which the question was asked. Additionally, the command offers the corresponding long variable as well as the link of 378 Chapter 7. Working with SOEP Documentation SOEP Survey Paper 1261 SOEPcompanion, Release 2023 the displayed variable to the documentation at paneldata.org. Conversely, with long data, you receive the wave-specific input variables and datasets used to generate the long-variable. 7.5. Working with SOEPhelp 379 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 Since our recent wave (v35) a new stata command option is being introduced. With soephelp, search (string) you reveive a list of variables that contain the respective word or label you are looking for. 380 Chapter 7. Working with SOEP Documentation SOEP Survey Paper 1261 SOEPcompanion, Release 2023 For example, you are interestet in variables regarding children in a household. With soephelp, search (child) you are able to see all variables having the word child in their label. To receive more details on the list of variables, use soephelp, search (child) verbose. Now you have the possibility to click on a variable and a new window opens up with details on the variable, like the question asked in the latest questionnaire, the question`s source, the long or core variable, depending on the data format. To use this option in english, add en at the end of the option. For example, soephelp, search (child) verbose en. SOEPhelp is directly linked to the SOEPcompanion. Contributions Contributions of all sorts are very welcome. Issues and requests can be reported to: Hans Walter Steinhauer for R and Marvin Petrenz for STATA Last change: Mar 28, 2023 7.5. Working with SOEPhelp 381 SOEP Survey Paper 1261 SOEPcompanion, Release 2023 7.6 Working with Metadata-based Questionnaires Metadata-based questionnaires make it considerably easier to find the variables of interest from the perspective of the questionnaire. Each of the generated PDFs reflects a questionnaire. With the help of these documents the user learns which questions have been asked in the respective sample and in which sequence. In addition, the documents make it clear what the question variable is called and which dataset it can be found in. The example shows question 5 from the individual questionnaire of SOEP-Core, which can be found in the data set bhp under the variable name bhp_05. 1. Example: Integrated Variable Let’s say you’re interested in finding out about refugees’ general life satisfaction. Search the questionnaire to find which refugees were surveyed for a second time in 2017. You’ll find what you’re looking for under question Q518. Below the question is the information on the name of the variable and the dataset where it is found. 382 Chapter 7. Working with SOEP Documentation SOEP Survey Paper 1261 SOEPcompanion, Release 2023 The general satisfaction with life can be found in the dataset bhp under the name bhp_205. 2. Example: Additional Variable Let’s say you’re interested in finding out about countries or origin. You want to know specifically how connected respondents feel to their country of origin. You’ll find the question in the questionniare given to refugees participating in the survey for the second time or more under question number Q480. The information on the question is stored in the data file bhp under the name bhp_480_q57. The name indicates that the question is not in the samples A-M2 because it has the suffix _q57. This does not preclude the question from being further down the integration hierarchy in questionnaires. Last change: Mar 28, 2023 7.6. Working with Metadata-based Questionnaires 383 SOEP Survey Paper 1261 CHAPTER EIGHT CONTACT INFORMATION The first version of the SOEPcompanion (formerly Desktop Companion) was published as a PDF document by John P. Haisken-DeNew and Joachim R. Frick in September 1996. It was originally intended to give novice users a broad introduction in understanding the SOEP, its structure, depth, and research potential. The Desktop Companion was updated several times between 1996 and 2005. The first major change came in 2014, when Jan Goebel and Mathis Schröder decided to shorten the Desktop Companion to its most important content and make it web-based. The new, completely edited version of the SOEPcompanion (formerly Desktop Companion) has a strong focus on the use of the SOEP-Core data from the perspective of a data user who has received our most recent data release from the SOEP Research Data Center. This new version is not only a web-based documentation, we also offer it as a download. Address: SOEP, DIW Berlin, Mohrenstraße 58, 10117 Berlin, Germany Homepage: http://www.diw.de/soep E-Mail: [email protected] SOEPhotline: +49 30 89789-292 Developers: Selin Kara, Stefan Zimmermann 384 SOEP Survey Paper 1261